Seth Godin got in on the action a bit, in making fun of Elon Musk's fancy new truck.
Regardless of what his choice to throw a ball at a window of his brand new truck says about his showmanship skills, the big technology development of the future is related to transportation. As well as mass transit works in places with high population density and a large number of tourists such as New York and San Francisco, most places don't have enough of either of those two items to make mass transit really work. This means self-driving cars will be the real growth area.
I was reading the following article...
https://www.technologyreview.com/s/613399/the-three-challenges-keeping-cars-from-being-fully-autonomous/
...and realized not too far into it that we were just talking about the triple constraints. The three big challenges are that they must be safe, usable, and affordable. They go on to discuss that you could make a vehicle so safe that it would be unusable, since it wouldn't drive very fast or be fuel efficient. This is similar to how the most secure computer is one that is not connected to the internet and with no keyboard or monitor.
But for a vehicle to be useful/usable, some safety constraints have to give way. If we push on both the usability and safety levers at the same time, the cost will be through the roof. Cheap and usable? Not safe. Cheap and safe? Not usable. Safe and usable? Not cheap.
Showing posts with label Research. Show all posts
Showing posts with label Research. Show all posts
Saturday, November 30, 2019
Thursday, August 31, 2017
That's So Meta
Meta-cognition is an important tool in the education world. Adding the prefix meta- to a term refers to additional information about that item.
Meta-data means data about data. If you have data stored in a spreadsheet or database, the meta-data could be things like what types of characters are allowed in each field, which items are required or optional, how many records are currently stored, and so on.
A meta-analysis is an analysis of other analyses. When a scientist writes a research paper, they collect data and do some type of analysis. The meta-analysis is then analyzing the data presented in many published research papers to see if they are consistent or measuring different things.
Cognition is understanding. So meta-cognition is understanding how to gain or measure understanding.
There are different types of meta-cognition discussed by Anderson & Krathwohl (2001), including strategic knowledge, application of cognitive tasks, and self-knowledge.
Strategic knowledge is simply being aware of various tools that can be used to learn - flash cards, mnemonics, mind maps, note taking, watching videos, physical practice, and so on.
The appropriate contextual application of the items listed above is the next one. If you're learning multiplication tables, flash cards serve as a useful tool to help learn those math facts to automaticity. If you're learning how to calculate an integral, a flash card would not be so helpful, since it's more about learning to apply the concepts to whatever numbers you are given. Don't use the wrong tool for the wrong type of learning. Usually the higher you go up Bloom's Taxonomy (remember, understand, apply, analyze, evaluate, create), you need more advanced strategic tools, or you may even create your own new method of learning.
The last one is understanding one's own understanding. This can include a couple levels - what methods of learning tend to be most effective for that particular individual and what the individual knows or doesn't know. A learner who is bad at math and is aware of that fact will take a more structured approach to find and utilize the best available tools for learning math or may choose a program of study that avoids the need to learn math. Someone who is a bad singer but doesn't realize it, may not ever take voice lessons, because they don't realize they need it. It's one thing to know what each note on the keyboard is by name and know the corresponding notes on the staff. It's something else to be able to physically play the right notes on the keyboard when looking at the staff. A piano student who continues to practice with flash cards even though they are all memorized isn't self-aware to the point that they know they need to move on to a different type of practice.
Make sure you take some time in whatever instruction you are doing to build the learner's meta-cognition at all levels. They should know what tools are available to them to learn. They should understand what each tool is used for. They should be able to reflect on their own application of those tools and how they work with their existing understanding in order to be willing to use the right tool at the right time for them.
Anderson, L.W. & Krathwohl, D.R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: Longman.
Meta-data means data about data. If you have data stored in a spreadsheet or database, the meta-data could be things like what types of characters are allowed in each field, which items are required or optional, how many records are currently stored, and so on.
A meta-analysis is an analysis of other analyses. When a scientist writes a research paper, they collect data and do some type of analysis. The meta-analysis is then analyzing the data presented in many published research papers to see if they are consistent or measuring different things.
Cognition is understanding. So meta-cognition is understanding how to gain or measure understanding.
There are different types of meta-cognition discussed by Anderson & Krathwohl (2001), including strategic knowledge, application of cognitive tasks, and self-knowledge.
Strategic knowledge is simply being aware of various tools that can be used to learn - flash cards, mnemonics, mind maps, note taking, watching videos, physical practice, and so on.
The appropriate contextual application of the items listed above is the next one. If you're learning multiplication tables, flash cards serve as a useful tool to help learn those math facts to automaticity. If you're learning how to calculate an integral, a flash card would not be so helpful, since it's more about learning to apply the concepts to whatever numbers you are given. Don't use the wrong tool for the wrong type of learning. Usually the higher you go up Bloom's Taxonomy (remember, understand, apply, analyze, evaluate, create), you need more advanced strategic tools, or you may even create your own new method of learning.
The last one is understanding one's own understanding. This can include a couple levels - what methods of learning tend to be most effective for that particular individual and what the individual knows or doesn't know. A learner who is bad at math and is aware of that fact will take a more structured approach to find and utilize the best available tools for learning math or may choose a program of study that avoids the need to learn math. Someone who is a bad singer but doesn't realize it, may not ever take voice lessons, because they don't realize they need it. It's one thing to know what each note on the keyboard is by name and know the corresponding notes on the staff. It's something else to be able to physically play the right notes on the keyboard when looking at the staff. A piano student who continues to practice with flash cards even though they are all memorized isn't self-aware to the point that they know they need to move on to a different type of practice.
Make sure you take some time in whatever instruction you are doing to build the learner's meta-cognition at all levels. They should know what tools are available to them to learn. They should understand what each tool is used for. They should be able to reflect on their own application of those tools and how they work with their existing understanding in order to be willing to use the right tool at the right time for them.
Anderson, L.W. & Krathwohl, D.R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: Longman.
Tuesday, December 16, 2014
Information Systems Success Models - An Annotated Bibliography
DeLone,W.H. & McLean, E.R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1).
IS success is multidimensional and interdependent, so interactions between success dimensions need to be isolated. Success dimensions should be based on goals of the research as well as proven measures where available. The number of factors should be minimized. The key factors included in the model include system quality, information quality, system use, user satisfaction, individual impacts, and organizational impact.
Rai, A., Lang, S.S., & Welker, R.B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1).
IS success models are compared. One major factor that differs among models is the category of IS Use. Some models include Use as a process since it is a prerequisite to other factors, others an indicator of success since people won’t use a system if they haven’t determined it will be useful to them, and of course perceived usefulness vs. measured use. The Technology Acceptance Model suggests that perceived usefulness and ease of use directly impact user behavior and system use.
Seddon, P.B. (1997). A respecification and extension of DeLone and McLean’s model of IS success. Information Systems Research, 8(September).
Standard variance models assert that variance in independent variables predicts variance in dependent variables. Process models, on the other hand, posit that not only are the occurrence of events necessary but that it is a particular sequence of events that leads to a change in the dependent variable. The presented IS success model removes the process component of the DeLone and McLean’s model. The problematic model contained three meanings of information system use. One meaning is that use provides some benefit to the user. A second, invalid, meaning presented use as a dependent variable of future use (i.e., if the user believes the system will be useful in the future, they will use it now). The third, also invalid, is that use is an event in the process that leads to individual or organizational impact. The proposed model links measures of system and information quality to perceived usefulness and user satisfaction, which in turn leads to expectations of future system usefulness and then use. Observing benefits to other individuals, organizations, and society also impact perceived usefulness and user satisfaction regardless of system or information quality.
Velasquez, N.F., Sabherwal, R., & Durcikova, A. (2011). Adoption of an electronic knowledge repository: A feature-based approach. Presented at 44th Hawaii International Conference on System Sciences, 4-7 January 2011, Kauai, HI.
This article discusses the types of use for knowledge base users. It utilized a cluster analysis to come up with three types of users. This included Enthusiastic Knowledge Seekers, Thoughtful Knowledge Providers, and Reluctant Non-adopters. Enthusiastic Knowledge Seekers made up the largest group at 70%. They had less knowledge and experience and shared little if anything of their own but considered the knowledgebase articles to be of high quality and very useful. The thoughtful knowledge providers, 19% of the users, submitted quality articles to the knowledgebase, enjoy sharing their knowledge with others, had moderate experience, and were intrinsically motivated. The smallest group, Reluctant Non-adopters at 11%, were experts who were highly experienced and adept at knowledge sharing but lacked the time or intrinsic motivation to do contribute meaningfully. They considered the knowledgebase to be low quality and did not consider it worth their time to work on improving it.
IS success is multidimensional and interdependent, so interactions between success dimensions need to be isolated. Success dimensions should be based on goals of the research as well as proven measures where available. The number of factors should be minimized. The key factors included in the model include system quality, information quality, system use, user satisfaction, individual impacts, and organizational impact.
Rai, A., Lang, S.S., & Welker, R.B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1).
IS success models are compared. One major factor that differs among models is the category of IS Use. Some models include Use as a process since it is a prerequisite to other factors, others an indicator of success since people won’t use a system if they haven’t determined it will be useful to them, and of course perceived usefulness vs. measured use. The Technology Acceptance Model suggests that perceived usefulness and ease of use directly impact user behavior and system use.
Seddon, P.B. (1997). A respecification and extension of DeLone and McLean’s model of IS success. Information Systems Research, 8(September).
Standard variance models assert that variance in independent variables predicts variance in dependent variables. Process models, on the other hand, posit that not only are the occurrence of events necessary but that it is a particular sequence of events that leads to a change in the dependent variable. The presented IS success model removes the process component of the DeLone and McLean’s model. The problematic model contained three meanings of information system use. One meaning is that use provides some benefit to the user. A second, invalid, meaning presented use as a dependent variable of future use (i.e., if the user believes the system will be useful in the future, they will use it now). The third, also invalid, is that use is an event in the process that leads to individual or organizational impact. The proposed model links measures of system and information quality to perceived usefulness and user satisfaction, which in turn leads to expectations of future system usefulness and then use. Observing benefits to other individuals, organizations, and society also impact perceived usefulness and user satisfaction regardless of system or information quality.
Velasquez, N.F., Sabherwal, R., & Durcikova, A. (2011). Adoption of an electronic knowledge repository: A feature-based approach. Presented at 44th Hawaii International Conference on System Sciences, 4-7 January 2011, Kauai, HI.
This article discusses the types of use for knowledge base users. It utilized a cluster analysis to come up with three types of users. This included Enthusiastic Knowledge Seekers, Thoughtful Knowledge Providers, and Reluctant Non-adopters. Enthusiastic Knowledge Seekers made up the largest group at 70%. They had less knowledge and experience and shared little if anything of their own but considered the knowledgebase articles to be of high quality and very useful. The thoughtful knowledge providers, 19% of the users, submitted quality articles to the knowledgebase, enjoy sharing their knowledge with others, had moderate experience, and were intrinsically motivated. The smallest group, Reluctant Non-adopters at 11%, were experts who were highly experienced and adept at knowledge sharing but lacked the time or intrinsic motivation to do contribute meaningfully. They considered the knowledgebase to be low quality and did not consider it worth their time to work on improving it.
Thursday, December 4, 2014
Cluster Analysis and Special Probability Distributions - An Annotated Bibliography
Antonenko, P., Toy, S., & Niederhauser, D. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398.
Server log data from online learning environments can be analyzed to examine student behaviors, in terms of pages visited, length of time on a page, order of links clicked, and so on. This analysis is less cognitively taxing to the student than think aloud techniques and to the researcher since there is no coding of behaviors involved. Cluster analysis groups cases such that they are very similar within the cluster and dissimilar to other cases outside the cluster across target variables. It is related to factor analysis, where regression models are created based on a set of variables across cases, but in cluster analysis, cases are then grouped. Proximity indices (squared Euclidean distance or sum of the squared differences across variables) are calculated for every pair of cases. Squaring makes them all positive and accentuates the outliers. Various clustering algorithms are available to then group similar cases. Ward’s is a hierarchical clustering technique that combines cases one at a time from n clusters to 1 cluster and determines which minimizes the standard error, and is used when there is no preconceived idea about the likely number of clusters. Using k-means clustering, a non-hierarchical techniques, an empirical rationale for a predetermined number of clusters is tested. It may also be used when there is a large sample size in order to increase efficiency; if no empirical basis exists, the model is run on 3, 4, and 5 clusters. The method calculates k centroids and associates cases with the closest centroid, repeating until the standard error is minimized by allowing cases to move to a different centroid. It may also be possible to use two different kinds of techniques, for example, a Ward’s cluster analysis on a small sample followed by a k-means cluster analysis based on the findings from Ward’s. After determining the clusters, the characteristics of each cluster should be compared to ensure there is a meaningful difference among them and that there is a meaningful difference in the outcome based on their behaviors, since cluster analysis can find structures in data where none exists. ANOVA may then be used to determine for each cluster how much each variable contributes to variation in the dependent variable. It may be useful to use more than one technique and compare or average them, as different techniques may result in a variation in the results.
Bain, L.J. & Englehardt, M. (1991). Special probability distributions. In Introduction to probability and mathematical statistics (2nd Edition). Belmont, CA: Duxberry Press.
A Bernoulli trial has two discrete outcomes whose probabilities add up to 1. A series of independent Bernoulli trials forms a Binomial distribution, where the number of successes (or failures) are determined for n trials. A Hypergeometric distribution occurs when n samples are taken from a population of N+M without replacement. It can be useful for testing a batch of manufactured products for defects in order to accept or reject the batch. The Geometric Binomial distribution determines the minimum number of Bernoulli trials that must occur to achieve a success. The Negative Binomial distribution determines the minimum number of Bernoulli trials that must occur to achieve n successes. The Poisson distribution describes the probability of n independent successes occurring over a certain number of trials. The discrete uniform distribution allows for n possible values, each with equal probability of occurrence.
Blau, B.M., Brough, T.J., & Thomas, D.W. (2013). Corporate lobbying, political connections, and the bailout of banks. Unpublished manuscript, Department of Finance and Economics, Utah State University, Logan, UT.
When measuring a dependent variable with discrete values, an appropriate count regression framework must be used. Poisson, negative binomial, and OLS are possible models to use. Poisson regression uses a distribution where the mean is equal to its variance. If the distribution is over-dispersed or significantly greater than 0, Poisson will not work. No discussion of when negative binomial or OLS work.
Collins, L.M. & Lanza, S.T. (2010). Latent class and latent transition analysis for the social, behavioral, and health sciences. New York: Wiley. Latent variables are unobserved but predicted by the observation of multiple observed variables. The latent variable is presumed to cause the observed indicator variables. Different models are used, depending on whether the observed and latent variables are discrete or continuous. Using a discrete latent variable helps organize complex arrays of categorical data. A given construct may be measured using either continuous or discrete variables, so the method used when there is a choice should be based on which best helps address the research questions. When cases are placed into classes, the classes are named by the researcher based on their similar characteristics.
Fisher, W.D. (1958). On grouping for maximum homogeneity. Journal of the American Statistical Association, 53, 789-798.
Grouping or clustering is a useful tool for distinguishing sets of cases based either on prior theory of what the groups should entail or with no initial structure in mind. Combining the groups has a goal of minimizing the variance or error sum of squares. For some small cases, a visual inspection of data may allow the researcher to come up with the clusters. In large data sets with evenly dispersed data, this is difficult or impossible.
Francis, B. (2010). Latent class analysis methods and software. Presented at 4th Economic and Social Research Council Research Methods Festival, 5 - 8 July 2010, St. Catherine’s College, Oxford, UK.
Latent class cluster analysis assigns cases to groups based on statistical likelihood; they do not have to be assigned to discrete classes. K-means clustering is problematic, since the number of groups has to be specified a priori, cases are assigned to unique clusters, and only allows continuous data.
Gardner, W., Mulvey, E.P., & Shaw, E.C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin 118(3).
Researchers often use suboptimal strategies when analyzing count data, such as artificially breaking down counts into categories of 5 or 10, but this loses data and statistical power. Another ineffective strategy is to use regular linear regression or OLS. Using OLS, illogical values, such as negatives will be predicted, and the model’s variance of values around the mean is not likely to fit well. Another problem with OLS is heteroscedastic error terms, where larger values will have larger variances and smaller values small variances. Nonlinear models that allow for only positive values and describe likely dispersion about the mean must be used. Poisson places restrictive assumptions on the size of the variance. The Overdispersed Poisson model corrects for the large variances that are common. The negative binomial is another option. In the regular Poisson model, truncated extreme tail values could lead to underdispersion and a large number of high values could lead to overdispersion. An overdispersion parameter is calculated by dividing Pearson’s chi-squared by the degrees of freedom and then the overdisperson parameter is multiplied by the mean. The negative binomial model includes a random component that accounts for individual variances. The negative binomial model allows one to estimate the probability distribution, where the overdispersed Poisson does not.
Osgood, D.W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology 16(1).
The normal approach to analyze per capita rates of occurrence is to use the OLS model. However, OLS does not provide an effective model when recording a small number of events. For large populations, OLS may work, but for a small number of events in a small population, the results is an overestimated rate of occurrence. Often small counts will be skewed with a floor of 0. The Poisson model corrects for many of these issues with OLS; however, the unlikely assumption of the Poisson’s mean equaling the variance must hold. Due to individual variations and correlation between observed values and variance, overdispersion is common. Adjusting the standard errors and thus t-test results for the overdispersion helps correct the model. The negative binomial model combines the Poisson distribution with a gamma distribution that accounts for unexplained variation.
Romesburg, H.C. (1990). Cluster Analysis for Researchers. Malabar, FL: Robert E. Krieger Publishing Company.
The steps in doing cluster analysis begin with creating the data matrix, including objects and their attributes. The objective is to determine which objects are most similar based on those attributes. An optional step is to standardize the data matrix. A resemblance matrix is then calculated, showing for each pair of objects a similarity coefficient, such as the Euclidean distance. Based on the similarity coefficient, a tree is created by combining similar objects and comparing their average to the other existing objects. Then rearrange objects in the data matrix to show the closest objects next to each other.
Velasquez, N.F., Sabherwal, R., & Durcikova, A. (2011). Adoption of an electronic knowledge repository: A feature-based approach. Presented at 44th Hawaii International Conference on System Sciences, 4-7 January 2011, Kauai, HI.
This article discusses the types of use for knowledge base users. It utilizes a cluster analysis to come up with three types of users. Clustering methods compared were Ward’s, between-groups linkage, within-groups linkage, centroid clustering, and median clustering and the one with the best fit was used.
Wang, W. & Famoye, F. (1997). Modeling household fertility decisions with generalized Poisson regression. Journal of Population Economics 10. Poisson and negative binomial models account for non-negative counts of discrete occurences. The Poisson model requires that the mean and variance of the dependent variable are equal, which is rarely true. This leads to a consistent model but invalid standard errors. The negative binomial model handles counts with overdispersion. When underdispersion is present, a generalized Poisson regression model may be used. Generalized Poisson handles both overdispersion and underdispersion.
Ward, J. H., Jr. (1963), Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, 48, 236–244.
Ward describes a clustering technique that allows for grouping with respect to many variables in such a way that minimizes the loss in each group. Traditional statistics would take a group of numbers, find the mean, and then calculate the error sum of squares for all cases and the one mean. By grouping, the ESS will be minimized as they are compared to the group means. The appropriate number of groups can be determined in the grouping process rather than needing to specify it in advance.
Server log data from online learning environments can be analyzed to examine student behaviors, in terms of pages visited, length of time on a page, order of links clicked, and so on. This analysis is less cognitively taxing to the student than think aloud techniques and to the researcher since there is no coding of behaviors involved. Cluster analysis groups cases such that they are very similar within the cluster and dissimilar to other cases outside the cluster across target variables. It is related to factor analysis, where regression models are created based on a set of variables across cases, but in cluster analysis, cases are then grouped. Proximity indices (squared Euclidean distance or sum of the squared differences across variables) are calculated for every pair of cases. Squaring makes them all positive and accentuates the outliers. Various clustering algorithms are available to then group similar cases. Ward’s is a hierarchical clustering technique that combines cases one at a time from n clusters to 1 cluster and determines which minimizes the standard error, and is used when there is no preconceived idea about the likely number of clusters. Using k-means clustering, a non-hierarchical techniques, an empirical rationale for a predetermined number of clusters is tested. It may also be used when there is a large sample size in order to increase efficiency; if no empirical basis exists, the model is run on 3, 4, and 5 clusters. The method calculates k centroids and associates cases with the closest centroid, repeating until the standard error is minimized by allowing cases to move to a different centroid. It may also be possible to use two different kinds of techniques, for example, a Ward’s cluster analysis on a small sample followed by a k-means cluster analysis based on the findings from Ward’s. After determining the clusters, the characteristics of each cluster should be compared to ensure there is a meaningful difference among them and that there is a meaningful difference in the outcome based on their behaviors, since cluster analysis can find structures in data where none exists. ANOVA may then be used to determine for each cluster how much each variable contributes to variation in the dependent variable. It may be useful to use more than one technique and compare or average them, as different techniques may result in a variation in the results.
Bain, L.J. & Englehardt, M. (1991). Special probability distributions. In Introduction to probability and mathematical statistics (2nd Edition). Belmont, CA: Duxberry Press.
A Bernoulli trial has two discrete outcomes whose probabilities add up to 1. A series of independent Bernoulli trials forms a Binomial distribution, where the number of successes (or failures) are determined for n trials. A Hypergeometric distribution occurs when n samples are taken from a population of N+M without replacement. It can be useful for testing a batch of manufactured products for defects in order to accept or reject the batch. The Geometric Binomial distribution determines the minimum number of Bernoulli trials that must occur to achieve a success. The Negative Binomial distribution determines the minimum number of Bernoulli trials that must occur to achieve n successes. The Poisson distribution describes the probability of n independent successes occurring over a certain number of trials. The discrete uniform distribution allows for n possible values, each with equal probability of occurrence.
Blau, B.M., Brough, T.J., & Thomas, D.W. (2013). Corporate lobbying, political connections, and the bailout of banks. Unpublished manuscript, Department of Finance and Economics, Utah State University, Logan, UT.
When measuring a dependent variable with discrete values, an appropriate count regression framework must be used. Poisson, negative binomial, and OLS are possible models to use. Poisson regression uses a distribution where the mean is equal to its variance. If the distribution is over-dispersed or significantly greater than 0, Poisson will not work. No discussion of when negative binomial or OLS work.
Collins, L.M. & Lanza, S.T. (2010). Latent class and latent transition analysis for the social, behavioral, and health sciences. New York: Wiley. Latent variables are unobserved but predicted by the observation of multiple observed variables. The latent variable is presumed to cause the observed indicator variables. Different models are used, depending on whether the observed and latent variables are discrete or continuous. Using a discrete latent variable helps organize complex arrays of categorical data. A given construct may be measured using either continuous or discrete variables, so the method used when there is a choice should be based on which best helps address the research questions. When cases are placed into classes, the classes are named by the researcher based on their similar characteristics.
Fisher, W.D. (1958). On grouping for maximum homogeneity. Journal of the American Statistical Association, 53, 789-798.
Grouping or clustering is a useful tool for distinguishing sets of cases based either on prior theory of what the groups should entail or with no initial structure in mind. Combining the groups has a goal of minimizing the variance or error sum of squares. For some small cases, a visual inspection of data may allow the researcher to come up with the clusters. In large data sets with evenly dispersed data, this is difficult or impossible.
Francis, B. (2010). Latent class analysis methods and software. Presented at 4th Economic and Social Research Council Research Methods Festival, 5 - 8 July 2010, St. Catherine’s College, Oxford, UK.
Latent class cluster analysis assigns cases to groups based on statistical likelihood; they do not have to be assigned to discrete classes. K-means clustering is problematic, since the number of groups has to be specified a priori, cases are assigned to unique clusters, and only allows continuous data.
Gardner, W., Mulvey, E.P., & Shaw, E.C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychological Bulletin 118(3).
Researchers often use suboptimal strategies when analyzing count data, such as artificially breaking down counts into categories of 5 or 10, but this loses data and statistical power. Another ineffective strategy is to use regular linear regression or OLS. Using OLS, illogical values, such as negatives will be predicted, and the model’s variance of values around the mean is not likely to fit well. Another problem with OLS is heteroscedastic error terms, where larger values will have larger variances and smaller values small variances. Nonlinear models that allow for only positive values and describe likely dispersion about the mean must be used. Poisson places restrictive assumptions on the size of the variance. The Overdispersed Poisson model corrects for the large variances that are common. The negative binomial is another option. In the regular Poisson model, truncated extreme tail values could lead to underdispersion and a large number of high values could lead to overdispersion. An overdispersion parameter is calculated by dividing Pearson’s chi-squared by the degrees of freedom and then the overdisperson parameter is multiplied by the mean. The negative binomial model includes a random component that accounts for individual variances. The negative binomial model allows one to estimate the probability distribution, where the overdispersed Poisson does not.
Osgood, D.W. (2000). Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology 16(1).
The normal approach to analyze per capita rates of occurrence is to use the OLS model. However, OLS does not provide an effective model when recording a small number of events. For large populations, OLS may work, but for a small number of events in a small population, the results is an overestimated rate of occurrence. Often small counts will be skewed with a floor of 0. The Poisson model corrects for many of these issues with OLS; however, the unlikely assumption of the Poisson’s mean equaling the variance must hold. Due to individual variations and correlation between observed values and variance, overdispersion is common. Adjusting the standard errors and thus t-test results for the overdispersion helps correct the model. The negative binomial model combines the Poisson distribution with a gamma distribution that accounts for unexplained variation.
Romesburg, H.C. (1990). Cluster Analysis for Researchers. Malabar, FL: Robert E. Krieger Publishing Company.
The steps in doing cluster analysis begin with creating the data matrix, including objects and their attributes. The objective is to determine which objects are most similar based on those attributes. An optional step is to standardize the data matrix. A resemblance matrix is then calculated, showing for each pair of objects a similarity coefficient, such as the Euclidean distance. Based on the similarity coefficient, a tree is created by combining similar objects and comparing their average to the other existing objects. Then rearrange objects in the data matrix to show the closest objects next to each other.
Velasquez, N.F., Sabherwal, R., & Durcikova, A. (2011). Adoption of an electronic knowledge repository: A feature-based approach. Presented at 44th Hawaii International Conference on System Sciences, 4-7 January 2011, Kauai, HI.
This article discusses the types of use for knowledge base users. It utilizes a cluster analysis to come up with three types of users. Clustering methods compared were Ward’s, between-groups linkage, within-groups linkage, centroid clustering, and median clustering and the one with the best fit was used.
Wang, W. & Famoye, F. (1997). Modeling household fertility decisions with generalized Poisson regression. Journal of Population Economics 10. Poisson and negative binomial models account for non-negative counts of discrete occurences. The Poisson model requires that the mean and variance of the dependent variable are equal, which is rarely true. This leads to a consistent model but invalid standard errors. The negative binomial model handles counts with overdispersion. When underdispersion is present, a generalized Poisson regression model may be used. Generalized Poisson handles both overdispersion and underdispersion.
Ward, J. H., Jr. (1963), Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, 48, 236–244.
Ward describes a clustering technique that allows for grouping with respect to many variables in such a way that minimizes the loss in each group. Traditional statistics would take a group of numbers, find the mean, and then calculate the error sum of squares for all cases and the one mean. By grouping, the ESS will be minimized as they are compared to the group means. The appropriate number of groups can be determined in the grouping process rather than needing to specify it in advance.
Friday, November 21, 2014
Instructional Design - An Annotated Bibliography
Anderson, L.W. & Krathwohl, D.R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. New York: Longman.
Instructional objectives are the exposure to terminology and scaffolded practice that build towards educational objectives. Educational objectives are the mid-level competency, which is the level at which assessment should culminate. Global objectives are the connection to the workplace – what a professional in the field should be able to do, or at a minimum, the big picture goal of a degree program. It may be described in the other direction as a cognitive mapping process where global objectives are deconstructed into educational objectives and educational objectives into instructional objectives utilizing a template representing the six levels of the taxonomy: Remember, Understand, Apply, Analyze, Evaluate, and Create. Objectives are what a student should be able to do but not a specific instructional activity or assessment activity.
Dupin-Bryant, P.A. & DuCharme-Hansen, B.A. (2005). Assessing Student Needs in Web-Based Distance Education. International Journal of Instructional Technology & Distance Learning 2(1).
Student needs assessment helps the instructor plan to facilitate a course learning experience. Learning objectives may or may not already be in place when the needs assessment is carried out, but the needs assessment will help refine the instructional objectives that need to be included to determine where to start. Areas to assess student needs include: computer skills, learning styles, available resources, desired outcomes, and prior experience. Computer literacy may be taught to the entire class or just the group that needs it or integrated into other learning activities. There is a larger debate on the usefulness of learning styles inventories, but the important concept is to ensure a variety of types of content and activities are provided. Available resources are probably more important in web-based education but important in any environment – do students have the hardware, software, internet access, or otherwise to be able to participate fully in all class activities? Course objectives are one thing, but students may be looking to get something else out of the class. Always build on what the students have previous learned, whether from previous classes or from experience in the workplace.
Fisher, D. H. (2012). Warming up to MOOCs. The Chronicle of Higher Education. Retrieved March 19, 2013 from http://chronicle.com/blogs/profhacker/warming-up-to-moocs.
Hesitation to use materials available from other instructors in one’s own classroom may be due to insecurity around what others will think about using outsourced lectures and what to do with class time instead of lecturing. The author used the MOOC content as homework assignments, flipping the classroom to then allow for higher level discussions of the material, instead of just presentation of the material. By utilizing materials of other faculty and contributing back, the community of scholarship extends from the research component of the faculty role to include teaching, which is often ignored.
Fusch, D. (2012). Course materials for mobile devices: Key considerations. Higher Ed Impact. Retrieved March 19, 2012, from Academic Impressions http://www.academicimpressions.com/news/course-materials-mobile-devices-key-considerations.
People spend as much time reading on a digital screen as they do reading paper. The amount of content read through mobile devices will soon surpass what is read on a full size computer. Faculty need to consider the usability and accessibility of the learning resources they assign to ensure they can be effectively used on mobile devices. By assuming it will be accessed on a mobile device first, it’s easier to move from mobile to desktop than the other way around. Record short videos, don’t use PDFs, and break up the readings into smaller chunks. Copyright and licensing considerations are important, as different licensing may apply in the mobile realm. Using open content is one way to ensure it can be ported to other platforms.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86.
One school of thought is that students learn by discovering or constructing concepts themselves, while another school says that direct explanation of concepts and related metacognition is the most effective. The assumptions at play in the minimal guidance approach are that experiential, self-constructed learning is best. The authors put forward that an understanding of human cognitive architecture is needed to determine the most effective instructional methods. They explain that long term memory permits experts to pull from vast experience to recognize a situation and know what to do. Given the limitations of working memory, the authors explain that minimally guided instruction taxes the working memory while pushing little into long term memory. They quote Shulman’s explanation of the difference between content knowledge and pedagogical content knowledge and curricular knowledge (metacognition) as well as how one works in the field (epistemology). Studying worked examples reduces cognitive load for novice learners that are prepared appropriately for them. PBL research shows little or no gain in problem solving ability.
Reed, S. K. (2006). Cognitive architectures for multimedia learning. Educational Psychologist, 41(2), 87-98.
Six theories of multimedia learning are reviewed, the first three being memory studies and the last three in instructional contexts. Paivio’s Dual Coding Theory: visual imagery is an important method of coding concepts; dual coding refers to the use of both verbal coding and visual coding of semantic meaning. Baddeley’s Working Memory Model: verbal and visual coding, with a verbal focus on phonological learning; includes the need for a “central executive” that the learner uses to guide what modality to use in the moment; author later adds to the model the episodic buffer, where information from various modalities can be combined. Engelkamp’s Multimodal Theory: acting out what is being learned results in greater recall, as action implies understanding, assuming the action is relevant to the semantics. Sweller’s Cognitive Load Theory: Multimedia design may decrease extraneous cognitive load by integrating information that needs to be presented together, worked examples, and schemas; split-attention and redundancy effects are described. Mayer’s Multimedia Theory: utilizes recommendations from other models; seven principles – multimedia (learn better from pictures and words together), spatial contiguity (corresponding words and pictures should be close to each other), temporal contiguity (words and pictures should be presented simultaneously), coherence (extraneous words, pictures, and sounds should be excluded), modality (animation + narration > animation + text), redundancy (animation + narration > animation + narration + text), and individual differences (low-knowledge learners and high-spatial learners are more affected by multimedia presentation). Nathan’s ANIMATE Theory: visual representation through simulation to help model the solution to a problem.
Renninger, K. A. (2009). Interest and identity development in instruction: An inductive model. Educational Psychologist, 44(2), 105-118.
Instructional model that states both the interest and identity of a student are important to consider in developing learning activities. Interest relates to the desire of a learner to reengage with particular content after a previous experience. Identity is the learner’s self-representation as someone who engages with particular content. Interest needs to be cultivated and sustained throughout all stages of individual development. Interest requires some understanding of what it takes to engage, not just a baseless sense of euphoria around an interesting topic. It’s also important to consider interest in achievement, as opposed to interest in the actual content. Identity changes as learners mature and come to understand how much work is required to accomplish required education levels and goals.
Shute, V., Towle, B. (2003) Adaptive E-Learning. Educational Psychologist 38(2).
Early iterations of e-learning were concerned with simply getting information online, but the focus now is on improving learning and performance. In order to be the most effective for each individual learner, the characteristics of each learner should be assessed. One behavior a system should encourage is exploration, as students who explore and participate in optional material tend to perform better on assessments. The learner model represents what the individual learner knows, and the instructional model presents and assesses content in the most appropriate way. Adaptive e-learning should provide, not the same textbook in a scrolling page instead of a physical page, but rather dynamically ordered and filtered pages to present learners just what they need right when they need it.
Smith, L. (2003). Software Design. In Guidelines for Successful Acquisition and Management of Software Intensive Systems (4th ed.). Hill Air Force Base, UT: U.S. Air Force Software Technology Support Center.
Given the complex nature of programming, design is the key phase between gathering requirements and actual development. Design includes several iterations, including functional design (logic, desired outputs, rules, data organization, and user interface), system design (system specifications, software structure, security, and programming standards), and program design (software units, test plans, user documentation, install plan, training plan, and programmer manual). Design methods include structured design (functions and subroutines with an order), object oriented design (objects inherit from parents, changes can be pushed out to many related objects, and specifics about what happen in each object are not as important), and extreme programming (frequent code review, testing, and release iterations).
Stiggins, R. & DuFour, R. (2009). Maximizing the Power of Formative Assessments. Phi Delta Kappan 90(9). Retrieved January 31, 2013 from http://alaskacc.org/sites/alaskacc.org/files/Stiggins%20article.pdf.
Formative assessment helps to track individual student achievement and performance and drive continuous improvement. Common assessments are created by multiple faculty members teaching the same course. Common formative assessments can result in significant improvement of learning if they are specifically integrated into instructional decision making, high quality, and used to benefit student learning. Assessments may be at the classroom, school, or institutional level. No matter the level or how they are used, assessments need clear learning targets that are appropriately scaffolded and achievable, based on established standards, high quality and high fidelity, and are available in a timely and understandable form in order to help the learner do better next time. Common formative assessments can be used to determine how an individual student is doing as well as to compare classroom performance. The act of putting together a common assessment allows the conversation to happen regarding what is truly important to measure. The greater dialogue among faculty members results in a higher quality assessment than what any individual teacher might be able to create.
van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.
When introduced, cognitive load theory led to new types of instructional methods, such as providing many worked examples instead of problems to solve. The theory posited that long term memory is made up of schemas, which make meaning out of complex structures. Working memory is limited when dealing with new content but not limited when working with schemas from long term memory. Cognitive load theory deals with the processing of content in working memory to create schemas stored in long term memory. In order to take a dynamic approach, where instruction is automatically tailored to the learner, the knowledge of learners must be assessed and methods of promoting effective instruction for each group of learners are needed. Assessment should include ability to generate correct responses as well as the mental effort required to accomplish that.
White, B., Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40(4).
Metacognition is crucial in helping individuals learn through inquiry and work together in teams. Understanding how to use inquiry learning will help the learner be more effective in using inquiry learning strategies. Inquiry then includes inquiry about inquiry and inquiry about the domain of study. Advisors to help manage metacognition may be automated tutors or other learners.
Wiggins, G. & McTighe, J. (2006). Backward Design. In Understanding by Design (Expanded 2nd Edition). Upper Saddle Hill, NJ: Pearson.
When planning curriculum, it is important to pay attention to local and national standards but also to determining the specific understandings that are desired. The focus should be on learning rather than on teaching. By determining a larger purpose first, the best resources and activities can be selected to achieve the goal. Traditional design falls into the trap of providing interesting experiences that do not lead to specific desired achievements or that briefly cover all content without touching on any with enough depth to be meaningful. Start with desired goals and ask what appropriate evidence of achievement would look like and likewise what the assessment should consist of. Only after determining what the desired results are and what an appropriate assessment would look like can learning experiences be planned. Some sacred cows may be harmed in the process of ensuring all activities have a specific purpose with effectiveness in reaching our targets in mind. The textbook may become less important than its significant role in many classrooms.
Instructional objectives are the exposure to terminology and scaffolded practice that build towards educational objectives. Educational objectives are the mid-level competency, which is the level at which assessment should culminate. Global objectives are the connection to the workplace – what a professional in the field should be able to do, or at a minimum, the big picture goal of a degree program. It may be described in the other direction as a cognitive mapping process where global objectives are deconstructed into educational objectives and educational objectives into instructional objectives utilizing a template representing the six levels of the taxonomy: Remember, Understand, Apply, Analyze, Evaluate, and Create. Objectives are what a student should be able to do but not a specific instructional activity or assessment activity.
Dupin-Bryant, P.A. & DuCharme-Hansen, B.A. (2005). Assessing Student Needs in Web-Based Distance Education. International Journal of Instructional Technology & Distance Learning 2(1).
Student needs assessment helps the instructor plan to facilitate a course learning experience. Learning objectives may or may not already be in place when the needs assessment is carried out, but the needs assessment will help refine the instructional objectives that need to be included to determine where to start. Areas to assess student needs include: computer skills, learning styles, available resources, desired outcomes, and prior experience. Computer literacy may be taught to the entire class or just the group that needs it or integrated into other learning activities. There is a larger debate on the usefulness of learning styles inventories, but the important concept is to ensure a variety of types of content and activities are provided. Available resources are probably more important in web-based education but important in any environment – do students have the hardware, software, internet access, or otherwise to be able to participate fully in all class activities? Course objectives are one thing, but students may be looking to get something else out of the class. Always build on what the students have previous learned, whether from previous classes or from experience in the workplace.
Fisher, D. H. (2012). Warming up to MOOCs. The Chronicle of Higher Education. Retrieved March 19, 2013 from http://chronicle.com/blogs/profhacker/warming-up-to-moocs.
Hesitation to use materials available from other instructors in one’s own classroom may be due to insecurity around what others will think about using outsourced lectures and what to do with class time instead of lecturing. The author used the MOOC content as homework assignments, flipping the classroom to then allow for higher level discussions of the material, instead of just presentation of the material. By utilizing materials of other faculty and contributing back, the community of scholarship extends from the research component of the faculty role to include teaching, which is often ignored.
Fusch, D. (2012). Course materials for mobile devices: Key considerations. Higher Ed Impact. Retrieved March 19, 2012, from Academic Impressions http://www.academicimpressions.com/news/course-materials-mobile-devices-key-considerations.
People spend as much time reading on a digital screen as they do reading paper. The amount of content read through mobile devices will soon surpass what is read on a full size computer. Faculty need to consider the usability and accessibility of the learning resources they assign to ensure they can be effectively used on mobile devices. By assuming it will be accessed on a mobile device first, it’s easier to move from mobile to desktop than the other way around. Record short videos, don’t use PDFs, and break up the readings into smaller chunks. Copyright and licensing considerations are important, as different licensing may apply in the mobile realm. Using open content is one way to ensure it can be ported to other platforms.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75-86.
One school of thought is that students learn by discovering or constructing concepts themselves, while another school says that direct explanation of concepts and related metacognition is the most effective. The assumptions at play in the minimal guidance approach are that experiential, self-constructed learning is best. The authors put forward that an understanding of human cognitive architecture is needed to determine the most effective instructional methods. They explain that long term memory permits experts to pull from vast experience to recognize a situation and know what to do. Given the limitations of working memory, the authors explain that minimally guided instruction taxes the working memory while pushing little into long term memory. They quote Shulman’s explanation of the difference between content knowledge and pedagogical content knowledge and curricular knowledge (metacognition) as well as how one works in the field (epistemology). Studying worked examples reduces cognitive load for novice learners that are prepared appropriately for them. PBL research shows little or no gain in problem solving ability.
Reed, S. K. (2006). Cognitive architectures for multimedia learning. Educational Psychologist, 41(2), 87-98.
Six theories of multimedia learning are reviewed, the first three being memory studies and the last three in instructional contexts. Paivio’s Dual Coding Theory: visual imagery is an important method of coding concepts; dual coding refers to the use of both verbal coding and visual coding of semantic meaning. Baddeley’s Working Memory Model: verbal and visual coding, with a verbal focus on phonological learning; includes the need for a “central executive” that the learner uses to guide what modality to use in the moment; author later adds to the model the episodic buffer, where information from various modalities can be combined. Engelkamp’s Multimodal Theory: acting out what is being learned results in greater recall, as action implies understanding, assuming the action is relevant to the semantics. Sweller’s Cognitive Load Theory: Multimedia design may decrease extraneous cognitive load by integrating information that needs to be presented together, worked examples, and schemas; split-attention and redundancy effects are described. Mayer’s Multimedia Theory: utilizes recommendations from other models; seven principles – multimedia (learn better from pictures and words together), spatial contiguity (corresponding words and pictures should be close to each other), temporal contiguity (words and pictures should be presented simultaneously), coherence (extraneous words, pictures, and sounds should be excluded), modality (animation + narration > animation + text), redundancy (animation + narration > animation + narration + text), and individual differences (low-knowledge learners and high-spatial learners are more affected by multimedia presentation). Nathan’s ANIMATE Theory: visual representation through simulation to help model the solution to a problem.
Renninger, K. A. (2009). Interest and identity development in instruction: An inductive model. Educational Psychologist, 44(2), 105-118.
Instructional model that states both the interest and identity of a student are important to consider in developing learning activities. Interest relates to the desire of a learner to reengage with particular content after a previous experience. Identity is the learner’s self-representation as someone who engages with particular content. Interest needs to be cultivated and sustained throughout all stages of individual development. Interest requires some understanding of what it takes to engage, not just a baseless sense of euphoria around an interesting topic. It’s also important to consider interest in achievement, as opposed to interest in the actual content. Identity changes as learners mature and come to understand how much work is required to accomplish required education levels and goals.
Shute, V., Towle, B. (2003) Adaptive E-Learning. Educational Psychologist 38(2).
Early iterations of e-learning were concerned with simply getting information online, but the focus now is on improving learning and performance. In order to be the most effective for each individual learner, the characteristics of each learner should be assessed. One behavior a system should encourage is exploration, as students who explore and participate in optional material tend to perform better on assessments. The learner model represents what the individual learner knows, and the instructional model presents and assesses content in the most appropriate way. Adaptive e-learning should provide, not the same textbook in a scrolling page instead of a physical page, but rather dynamically ordered and filtered pages to present learners just what they need right when they need it.
Smith, L. (2003). Software Design. In Guidelines for Successful Acquisition and Management of Software Intensive Systems (4th ed.). Hill Air Force Base, UT: U.S. Air Force Software Technology Support Center.
Given the complex nature of programming, design is the key phase between gathering requirements and actual development. Design includes several iterations, including functional design (logic, desired outputs, rules, data organization, and user interface), system design (system specifications, software structure, security, and programming standards), and program design (software units, test plans, user documentation, install plan, training plan, and programmer manual). Design methods include structured design (functions and subroutines with an order), object oriented design (objects inherit from parents, changes can be pushed out to many related objects, and specifics about what happen in each object are not as important), and extreme programming (frequent code review, testing, and release iterations).
Stiggins, R. & DuFour, R. (2009). Maximizing the Power of Formative Assessments. Phi Delta Kappan 90(9). Retrieved January 31, 2013 from http://alaskacc.org/sites/alaskacc.org/files/Stiggins%20article.pdf.
Formative assessment helps to track individual student achievement and performance and drive continuous improvement. Common assessments are created by multiple faculty members teaching the same course. Common formative assessments can result in significant improvement of learning if they are specifically integrated into instructional decision making, high quality, and used to benefit student learning. Assessments may be at the classroom, school, or institutional level. No matter the level or how they are used, assessments need clear learning targets that are appropriately scaffolded and achievable, based on established standards, high quality and high fidelity, and are available in a timely and understandable form in order to help the learner do better next time. Common formative assessments can be used to determine how an individual student is doing as well as to compare classroom performance. The act of putting together a common assessment allows the conversation to happen regarding what is truly important to measure. The greater dialogue among faculty members results in a higher quality assessment than what any individual teacher might be able to create.
van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147-177.
When introduced, cognitive load theory led to new types of instructional methods, such as providing many worked examples instead of problems to solve. The theory posited that long term memory is made up of schemas, which make meaning out of complex structures. Working memory is limited when dealing with new content but not limited when working with schemas from long term memory. Cognitive load theory deals with the processing of content in working memory to create schemas stored in long term memory. In order to take a dynamic approach, where instruction is automatically tailored to the learner, the knowledge of learners must be assessed and methods of promoting effective instruction for each group of learners are needed. Assessment should include ability to generate correct responses as well as the mental effort required to accomplish that.
White, B., Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40(4).
Metacognition is crucial in helping individuals learn through inquiry and work together in teams. Understanding how to use inquiry learning will help the learner be more effective in using inquiry learning strategies. Inquiry then includes inquiry about inquiry and inquiry about the domain of study. Advisors to help manage metacognition may be automated tutors or other learners.
Wiggins, G. & McTighe, J. (2006). Backward Design. In Understanding by Design (Expanded 2nd Edition). Upper Saddle Hill, NJ: Pearson.
When planning curriculum, it is important to pay attention to local and national standards but also to determining the specific understandings that are desired. The focus should be on learning rather than on teaching. By determining a larger purpose first, the best resources and activities can be selected to achieve the goal. Traditional design falls into the trap of providing interesting experiences that do not lead to specific desired achievements or that briefly cover all content without touching on any with enough depth to be meaningful. Start with desired goals and ask what appropriate evidence of achievement would look like and likewise what the assessment should consist of. Only after determining what the desired results are and what an appropriate assessment would look like can learning experiences be planned. Some sacred cows may be harmed in the process of ensuring all activities have a specific purpose with effectiveness in reaching our targets in mind. The textbook may become less important than its significant role in many classrooms.
Monday, February 17, 2014
Help Seeking - An Annotated Bibliography
Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research 73(3).
Help seeking can be seen, not as dependence of the learner, but as self-regulated behavior that helps to develop independent ability. For this to be the case, the help seeking needs to be effective. There are various types of computer-based instruction, including intelligent tutoring systems (AI gives context-sensitive hints), computer assisted instruction (feedback on actions without AI to guide), educational hypermedia (cross-linked information), and problem/based systems (authentic problems with background information and hints about solving the problem). Help seeking model is presented: aware of need for help, decide to seek help, identify helpers, ask for help, and evaluate help. Many studies actually show ineffective use of computer based learning, but that on demand help does tend to help students learn better. Student prior knowledge is a major influence in student performance and success, both in terms of familiarity with the subject and the learning environment. Help seeking ability improves with age due to better ability to monitor one’s own performance. In terms of gender, males are less likely to seek help than females in traditional classroom environments, and while there is less research in computer based learning environments, similar results have been found. A focus on performance rather than learning can lead to avoidance of help seeking.
Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. International Review of Research in Open and Distance Learning 4(2).
Interaction may be defined as only between two people, but here they accept the definition that allows any people or objects to interact with and influence each other, so a student may interact with another student or may interact with content. It’s difficult to know for sure if interactions, as helpful as they may be, actually have educational value. Some students choose programs that minimize the amount of person to person interaction required. A high level of interaction with content, other students, or the teacher may be sufficient, even if the other forms are not present (although student/teacher interaction is perceived as the highest value). Student/content interactions can take the place of many person to person interactions in the right circumstances.
Azevedo, R., Moos, D.C., Greene, J.A., Winters, F.I., & Cromley, J.G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development 56(1).
This study compared self-regulated and externally facilitated learning with adolescents studying complex topics. If students lack metacognitive abilities, such as planning, setting goals, activating prior knowledge, and so on, ineffective strategies may lead to less effective use of online resources. The tutor provides individualized scaffolding to each student, that fades (although not completely away) during the course. Tutor-led scaffolding conditions helped students obtain a more complex mental model, as well as more declarative knowledge, and different metacognitive strategies were used by both groups. The study is fairly limited based on age, low prior knowledge, and relatively complex nature of the content.
Butler, R. (1998). Determinants of help seeking: Relations between perceived reasons for classroom help-avoidance and help-seeking behaviors in an experimental context. Journal of Educational Psychology 90(4).
This study dealt with help-avoidance. Students will seek less help for assessments identified as to test competence compared to assessments that are an opportunity to learn. This is because their reluctance is often due to perceptions that learning should be autonomous and that asking for help is evidence of incompetence. This can lead to students who do need help to seek covert help (cheating). Some students may ask for help in solving a problem because they simply want to finish, not necessarily learn anything. Students with ability-focused orientation asked fewer questions than those with autonomous or expedient orientations. Boys with ability-focused orientation cheated more often. One observation that wasn’t a specific purpose of the study was that teachers participating in the study created an environment more conducive to asking questions than is normally found in classrooms. Also limited due to young age of students.
Elen, J., Clarebout, G., Leonard, R., & Lowyck, J. (2007). Student-centred and teacher-centred learning environments: What students think. Teaching in Higher Education 12(1).
Balanced view includes sharing instructional tasks between teacher and student at different points in time. Transactional view is similar, in that student and teacher share responsibilities, but the teacher has the additional responsibility of monitoring and coaching the student through their part. Independent view claims that their roles are fundamentally different. The survey tended to confirm that student-centeredness and teacher-centeredness are not necessarily on the extreme ends of the continuum, so giving more power to students doesn’t necessarily mean the teacher’s job goes away completely. They can actually be mutually reinforcing.
Karabenick, S.A. (2011). Classroom and technology-supported help seeking: The need for converging research paradigms. Learning and Instruction 21(2).
Help seeking is more likely to occur in a context focused on learning and understanding than ones focused on ability or where public disclosure may be embarrassing. Differences exist between research in computer-mediated environments and traditional classrooms. When presenting new information, one study showed preferences for a more structured environment, but given the new methods for the teacher and new content for the students, the study’s results may not be applicable elsewhere. Motivational content may lead to additional help-seeking behaviors. Help-seeking is susceptible to social influence, even when not interacting with another person directly.
Lebak, K. & Tinsley, R. (2010). Can inquiry and reflection be contagious? Science teachers, students, and action research. Journal of Science Teacher Education, 21, 953-970.
Case studies of three science teachers who converted from a teacher-centered approach to an inquiry-based approach for student learning. Whether the teacher was unaware of a need for change, working with special needs students, or limited in the amount of time to conduct experiments, they all found students were more engaged by being hands-on. Peer reflection and feedback (of the teacher’s peers) was important in helping the teachers transform their classrooms. Both the students and teachers evolve dramatically during the conversion to inquiry learning.
Makitalo-Siegl, K. & Fischer, F. (2011). Stretching the limits in help-seeking research: Theoretical, methodological, and technological advances. Learning and Instruction 21(2).
As help seeking is a social behavior, some less socially oriented learners may avoid seeking help, so computer-based resources may reduce barriers that prevent face to face interactions. It is important to look at help seeking behaviors in a variety of environments, tied to various forms of instruction, with various types of resources available. In addition to studying the technology involved, it’s important to look at motivational and emotional dimensions.
Mercier, J. & Frederiksen, C. (2008). The structure of the help-seeking process in collaboratively using a computer coach in problem-based learning. Computers & Education 51(1).
There is little research on help-seeking in an online environment, as it is mostly in a social context like a classroom. Problems students may encounter in learning a new domain include not understanding the solution schema, not understanding the content, and making a mistake in the process. When problems occur, help can overcome the impasse. With a computer tutor, instead of having the expert monitor the student’s progress and needs, the student has to monitor his or her own progress and needs. Phases in the Mercier model: recognize impasse, diagnose impasse, establish specific need for help, find help, read and comprehend help, and evaluate help.
Peterson, S., & Palmer, L. (2011). Technology Confidence, Competence and Problem Solving Strategies: Differences within Online and Face-to-Face Formats. Journal of Distance Education, 25(2).
When students encounter a new problem, they often hesitate to participate because of a lack of confidence; however, research shows that that is the point where they need to engage with others, in order to solve a problem and move to more challenging tasks. Four problem solving strategies include: seeking instructor assistance, seeking peer assistance, further reading, and trial and error, all of which can be effective methods. One study showed that online students felt more comfortable asking for help than traditional students. In this study of university teacher education students, face to face students often waited for instructor assistance, while online students tended to do more trial and error or further reading, and the online students were more competent.
Roll, I., Aleven, V., McLaren, B.M., & Koedinger, K.R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction 21(2).
A tutoring system must be able to detect metacognitive errors and encourage appropriate behavior. Help-seeking advice from the tutoring system can improve such behaviors within other domains of study.
Ryan, A.M., Pintrich, P.R., & Midgley, C. (2001). Avoiding seeking help in the classroom: Who and why? Educational Psychology Review 13(2). The help seeking process starts when students realize there is a problem and then decide to seek help. Students may choose not to seek help because they believe they should not, that no one is competent to help, that it may take too long, or that it highlights one’s incompetence. Highly competent students are more likely to ask for help, because they don’t think others will think poorly of them for it; low achievers are more concerned about what others think.
Weerasinghe, T., Ramberg, R., & Hewagamage, K. (2012). Inquiry-Based Learning With or Without Facilitator Interactions. Journal of Distance Education, 26(2).
Inquiry-based learning promotes higher engagement and construction of knowledge in complex content areas. Teachers have an important role in encouraging participation in a community, but other types of interactions can be effective as well. The inquiry process includes four major phases: triggering event, exploration, integration, and resolution (see Gagne’s 9 Events). The study compared online course discussions with and without a teacher or TA present. They found that the dialogues in both cases students were able to attain high levels of interaction and inquiry and meaningful learning. If anything, when the facilitator was not present, students picked up the slack in terms of additional metacognitive activities.
Wood, D. (2009). Comments on learning with ICT: New perspectives on help seeking and information searching. Computers & Education 53(4).
Digital technologies have allowed for additional research into the area of help seeking, although there is little information so far that has been studied. It does seem clear that self-regulation is important, they need to be encouraged to use resources available to them, and students will be more successful as an independent learner if they paradoxically seek help when needed. While human facilitators are common and natural, it’s possible that automated recommender systems and knowledge bases may be as effective as those technologies become more robust.
Wood, D., Bruner, J.S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry 17(2).
Tutoring, where one member of a group knows how to do something others do not is a common feature of learning, especially with young children. Scaffolding, controlled by an adult, allows the child to solve a problem initially beyond his or her reach. One key that must exist is a recognition and understanding of a solution in order to be able to come up with one’s own solution. Younger children were as adept at recognizing appropriate solutions, although less adept at creating their own solutions. The tutor needs to understand both the task at hand and the characteristics of the tutee.
Help seeking can be seen, not as dependence of the learner, but as self-regulated behavior that helps to develop independent ability. For this to be the case, the help seeking needs to be effective. There are various types of computer-based instruction, including intelligent tutoring systems (AI gives context-sensitive hints), computer assisted instruction (feedback on actions without AI to guide), educational hypermedia (cross-linked information), and problem/based systems (authentic problems with background information and hints about solving the problem). Help seeking model is presented: aware of need for help, decide to seek help, identify helpers, ask for help, and evaluate help. Many studies actually show ineffective use of computer based learning, but that on demand help does tend to help students learn better. Student prior knowledge is a major influence in student performance and success, both in terms of familiarity with the subject and the learning environment. Help seeking ability improves with age due to better ability to monitor one’s own performance. In terms of gender, males are less likely to seek help than females in traditional classroom environments, and while there is less research in computer based learning environments, similar results have been found. A focus on performance rather than learning can lead to avoidance of help seeking.
Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. International Review of Research in Open and Distance Learning 4(2).
Interaction may be defined as only between two people, but here they accept the definition that allows any people or objects to interact with and influence each other, so a student may interact with another student or may interact with content. It’s difficult to know for sure if interactions, as helpful as they may be, actually have educational value. Some students choose programs that minimize the amount of person to person interaction required. A high level of interaction with content, other students, or the teacher may be sufficient, even if the other forms are not present (although student/teacher interaction is perceived as the highest value). Student/content interactions can take the place of many person to person interactions in the right circumstances.
Azevedo, R., Moos, D.C., Greene, J.A., Winters, F.I., & Cromley, J.G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development 56(1).
This study compared self-regulated and externally facilitated learning with adolescents studying complex topics. If students lack metacognitive abilities, such as planning, setting goals, activating prior knowledge, and so on, ineffective strategies may lead to less effective use of online resources. The tutor provides individualized scaffolding to each student, that fades (although not completely away) during the course. Tutor-led scaffolding conditions helped students obtain a more complex mental model, as well as more declarative knowledge, and different metacognitive strategies were used by both groups. The study is fairly limited based on age, low prior knowledge, and relatively complex nature of the content.
Butler, R. (1998). Determinants of help seeking: Relations between perceived reasons for classroom help-avoidance and help-seeking behaviors in an experimental context. Journal of Educational Psychology 90(4).
This study dealt with help-avoidance. Students will seek less help for assessments identified as to test competence compared to assessments that are an opportunity to learn. This is because their reluctance is often due to perceptions that learning should be autonomous and that asking for help is evidence of incompetence. This can lead to students who do need help to seek covert help (cheating). Some students may ask for help in solving a problem because they simply want to finish, not necessarily learn anything. Students with ability-focused orientation asked fewer questions than those with autonomous or expedient orientations. Boys with ability-focused orientation cheated more often. One observation that wasn’t a specific purpose of the study was that teachers participating in the study created an environment more conducive to asking questions than is normally found in classrooms. Also limited due to young age of students.
Elen, J., Clarebout, G., Leonard, R., & Lowyck, J. (2007). Student-centred and teacher-centred learning environments: What students think. Teaching in Higher Education 12(1).
Balanced view includes sharing instructional tasks between teacher and student at different points in time. Transactional view is similar, in that student and teacher share responsibilities, but the teacher has the additional responsibility of monitoring and coaching the student through their part. Independent view claims that their roles are fundamentally different. The survey tended to confirm that student-centeredness and teacher-centeredness are not necessarily on the extreme ends of the continuum, so giving more power to students doesn’t necessarily mean the teacher’s job goes away completely. They can actually be mutually reinforcing.
Karabenick, S.A. (2011). Classroom and technology-supported help seeking: The need for converging research paradigms. Learning and Instruction 21(2).
Help seeking is more likely to occur in a context focused on learning and understanding than ones focused on ability or where public disclosure may be embarrassing. Differences exist between research in computer-mediated environments and traditional classrooms. When presenting new information, one study showed preferences for a more structured environment, but given the new methods for the teacher and new content for the students, the study’s results may not be applicable elsewhere. Motivational content may lead to additional help-seeking behaviors. Help-seeking is susceptible to social influence, even when not interacting with another person directly.
Lebak, K. & Tinsley, R. (2010). Can inquiry and reflection be contagious? Science teachers, students, and action research. Journal of Science Teacher Education, 21, 953-970.
Case studies of three science teachers who converted from a teacher-centered approach to an inquiry-based approach for student learning. Whether the teacher was unaware of a need for change, working with special needs students, or limited in the amount of time to conduct experiments, they all found students were more engaged by being hands-on. Peer reflection and feedback (of the teacher’s peers) was important in helping the teachers transform their classrooms. Both the students and teachers evolve dramatically during the conversion to inquiry learning.
Makitalo-Siegl, K. & Fischer, F. (2011). Stretching the limits in help-seeking research: Theoretical, methodological, and technological advances. Learning and Instruction 21(2).
As help seeking is a social behavior, some less socially oriented learners may avoid seeking help, so computer-based resources may reduce barriers that prevent face to face interactions. It is important to look at help seeking behaviors in a variety of environments, tied to various forms of instruction, with various types of resources available. In addition to studying the technology involved, it’s important to look at motivational and emotional dimensions.
Mercier, J. & Frederiksen, C. (2008). The structure of the help-seeking process in collaboratively using a computer coach in problem-based learning. Computers & Education 51(1).
There is little research on help-seeking in an online environment, as it is mostly in a social context like a classroom. Problems students may encounter in learning a new domain include not understanding the solution schema, not understanding the content, and making a mistake in the process. When problems occur, help can overcome the impasse. With a computer tutor, instead of having the expert monitor the student’s progress and needs, the student has to monitor his or her own progress and needs. Phases in the Mercier model: recognize impasse, diagnose impasse, establish specific need for help, find help, read and comprehend help, and evaluate help.
Peterson, S., & Palmer, L. (2011). Technology Confidence, Competence and Problem Solving Strategies: Differences within Online and Face-to-Face Formats. Journal of Distance Education, 25(2).
When students encounter a new problem, they often hesitate to participate because of a lack of confidence; however, research shows that that is the point where they need to engage with others, in order to solve a problem and move to more challenging tasks. Four problem solving strategies include: seeking instructor assistance, seeking peer assistance, further reading, and trial and error, all of which can be effective methods. One study showed that online students felt more comfortable asking for help than traditional students. In this study of university teacher education students, face to face students often waited for instructor assistance, while online students tended to do more trial and error or further reading, and the online students were more competent.
Roll, I., Aleven, V., McLaren, B.M., & Koedinger, K.R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction 21(2).
A tutoring system must be able to detect metacognitive errors and encourage appropriate behavior. Help-seeking advice from the tutoring system can improve such behaviors within other domains of study.
Ryan, A.M., Pintrich, P.R., & Midgley, C. (2001). Avoiding seeking help in the classroom: Who and why? Educational Psychology Review 13(2). The help seeking process starts when students realize there is a problem and then decide to seek help. Students may choose not to seek help because they believe they should not, that no one is competent to help, that it may take too long, or that it highlights one’s incompetence. Highly competent students are more likely to ask for help, because they don’t think others will think poorly of them for it; low achievers are more concerned about what others think.
Weerasinghe, T., Ramberg, R., & Hewagamage, K. (2012). Inquiry-Based Learning With or Without Facilitator Interactions. Journal of Distance Education, 26(2).
Inquiry-based learning promotes higher engagement and construction of knowledge in complex content areas. Teachers have an important role in encouraging participation in a community, but other types of interactions can be effective as well. The inquiry process includes four major phases: triggering event, exploration, integration, and resolution (see Gagne’s 9 Events). The study compared online course discussions with and without a teacher or TA present. They found that the dialogues in both cases students were able to attain high levels of interaction and inquiry and meaningful learning. If anything, when the facilitator was not present, students picked up the slack in terms of additional metacognitive activities.
Wood, D. (2009). Comments on learning with ICT: New perspectives on help seeking and information searching. Computers & Education 53(4).
Digital technologies have allowed for additional research into the area of help seeking, although there is little information so far that has been studied. It does seem clear that self-regulation is important, they need to be encouraged to use resources available to them, and students will be more successful as an independent learner if they paradoxically seek help when needed. While human facilitators are common and natural, it’s possible that automated recommender systems and knowledge bases may be as effective as those technologies become more robust.
Wood, D., Bruner, J.S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry 17(2).
Tutoring, where one member of a group knows how to do something others do not is a common feature of learning, especially with young children. Scaffolding, controlled by an adult, allows the child to solve a problem initially beyond his or her reach. One key that must exist is a recognition and understanding of a solution in order to be able to come up with one’s own solution. Younger children were as adept at recognizing appropriate solutions, although less adept at creating their own solutions. The tutor needs to understand both the task at hand and the characteristics of the tutee.
Wednesday, November 20, 2013
CMC - An Annotated Bibliography
Anderson, T., Poellhuber, B., & McKerlich, R. (2010). Self-paced learners meet social software: An exploration of learners’ attitudes, expectations, and experience. Online Journal of Distance Learning Administration XIII(III).
Most traditional and online courses use a group-pacing model, but self-paced models give students more access and control. Given the increased control self-pacing provides, there is a corresponding lower social interaction and higher rate of attrition. Varying backgrounds of students do correlate with persistence, but the design of course materials and support provided to students is all that can be adjusted by the school/instructor. Social networking and other communication technologies allow us provide more learning support and interaction. Social software should provide sociability, a sense of trust and belonging; interaction, how connected students are with teachers and peers; and peer collaboration, constructivist learning with cohorts or informal groups. Many students are interested in interacting with peers, so course technology design should make this easier to do. Connectivist learning models have many of the answers. Survey of student interest in social technologies revealed a variety of responses. They are largely confident in using the internet but have only moderate exposure to many social media tools, with older students being less familiar than younger students. About half the students are interested in interacting with other students, and half are not, thus online collaborative activities should be compelling but not required.
Arbaugh, J.B. (2008) Does the community of inquiry framework predict outcomes in online MBA courses? International Review of Research in Open and Distance Learning 9(2).
The CoI framework describes teaching presence, social presence, and cognitive presence. Teaching presence includes course design and organization, direct instruction, and instructor facilitation. Social presence includes group cohesion, open communication, and affective expression. Cognitive presence includes a process where a problem is identified, explored, meaning constructed, and then the problem is solved. Study findings showed that the model accounted for 54% of the variance in student learning, with teaching presence and cognitive presence the strongest predictors of success. Social presence was a smaller predictor but still necessary to support the other two. Simpler or more familiar learning environments should produce higher cognitive gains (CLT).
de Souza, C. S., & Preece, J. (2004). A framework for analyzing and understanding online communities. Interacting with Computers, 16(3), 579-610.
This article points out two components by which an online community can be assessed: sociability (people, purposes, and policies) and usability (software). In their framework, these two components have to be aligned to produce success. Any community (whether online, offline, or a hybrid) will have sociability factors that change as the people (or purposes or policies) in the community change. For any online community, the software has to work with those people, purposes, and policies. They continue on to discuss Semiotics and HCI and how communication takes place among users and designers. The important part is that everyone is communicating all the time, but the message doesn't always get across how we expect it.
Gagne, R.M., Wager, W.W., Golas, K.C., & Keller, J.M. (2005). Online learning. In Principles of Instructional Design (5th Edition). Belmont, CA: Wadsworth.
While many instructional design theories related to offline computer-based instruction also apply to online learning, the networked nature of online learning means that there are additional affordances for students and teachers. Students can be located anywhere around the world and still participate with others and costs for delivering instruction are lower. Specialized software is not needed, as everything can be accessed through a web browser, content is delivered more efficiently, courses are flexible in allowing both self-paced and group-paced activities, updating content is instant, web analytics provide robust tracking of behaviors, and interactions among others can be increased. However, there may also be issues if students are overwhelmed with the amount of information without a clear path to follow through it all, and if social connections are not cultivated, individuals can be left on their own. If learners are required to access materials synchronously, that may cause scheduling conflicts; on the other hand, if learners are required to access materials on their own personal time, they may be unsatisfied with required training taking away from personal activities. Computer literacy is an important consideration. Learning management systems and other collaborative environments can support group work and constructivist teaching methods. Collaboration can be synchronous or asynchronous and text- or multimedia-based. Blended learning, with a combination of online and face to face activities is also an option.
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105.
The Community of Inquiry framework refers to three major components - cognitive presence, social presence, and teaching presence. Teaching presence is getting the conversation started and keeping it focused, cognitive presence is the desire to exchange ideas, and social presence is how well everyone gets along.
Lowenthal, P.R. (2010). The evolution and influence of social presence theory on online learning. In Online Learning and Adult Education. Hershey, PA: Information Science Reference.
The internet can be social and bring people together or separate and isolate them. It can also cause addiction and dependence. By studying the sociality of a network, we can better understand and deal with the negative issues while promoting the positives. Early research in computer mediated communication (CMC) showed that it did some things well and other things not as well. The theory of social presence was developed to help determine the quality of communication between people. Mediums with a high degree of social presence are considered warm and personal. The meaning surrounding a CMC message depends highly on context, thus initial research in primarily business environments made educational researchers and practitioners wary of trying out CMC, although those fears have been generally unfounded. Additional research in social presence theory has supported the principle that the extent to which a given aspect of a communication medium (visual, audio, etc.) is important depends on the task for which it is being used. Media richness is the extent to which a communication carries understanding and data. While some cues that promote richness are filtered out in CMC, users of the system find ways to convey that same richness, such as a winking emoticon where a physical wink is not possible. Future research in this area will move from online learning to the blurring of boundaries between the classroom and personal communications in social media.
McLuhan, M. (1964). The medium is the message. In Understanding Media: The Extensions of Man. New York: Signet.
The personal and social influences of any medium (or technology) can be positive or negative. Technologies shape us and affect our interactions. The example given is that the railway sped up expansion and growth of society, leading new cities and types of work, where the expansion of the airplane undoes the work the railroad did and builds different types of cities and work. The technology of the light bulb allows certain activities to take place, which could not otherwise, thus those activities are the message inherent in that technology. “The message, it seemed, was the content, as people used to ask what a painting was about. Yet they never thought to ask what a melody was about, nor what a house or a dress was about.” The content of the message often distracts from the real substance which should be the medium and the structural changes it makes in us and in our society.
Vonderwell, S. (2002). An examination of asynchronous communication experiences and perspectives of students in an online course: A case study. The Internet and Higher Education, 6, 77-90.
This study looked at how asynchronous communication affects student learning and whether or not it enhances it. Interaction among students and between students and teachers is an important part of the learning process. Pulling apart and putting back together beliefs and knowledge help promote collective knowledge building. Introverts tend to participate more in CMC environments, while extroverts tend to participate less. In the study, an online technology course for education majors, the students appreciated the opportunity to interact electronically with the instructor and that they did not worry as much about what other students thought of them as they do in a traditional classroom. It seemed easier to ignore questions in the online classroom that would have to be immediately answered in a face to face environment, so some negative aspects frustrated some students. Response time by the instructor tended to slow down toward the end of the semester, where responses and grading were fast at the beginning of the semester. Some groups had a hard time collecting information needed to write group papers, while others felt that they learned from each other and collaborated well. Students attempted to ensure their writing was more clear to avoid miscommunication issues. A balance should exist between quick instructor feedback and giving students time to carry out their own inquiry.
Walther, J.B. (1996) Computer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction. Communication Research 23(1).
The initial line of research into CMC showed that the medium is not rich or personal enough to convey important interpersonal communication or effective task-related communications. So if it’s not good for tasks or socializing, what is it used for? In early studies with zero history groups using CMC, it was effective as a task-centered communication mechanism. Low social interaction in these zero history groups led CMC to be pegged as depersonalized. For groups with an impersonal focus, this is actually a good thing. Task orientation without affect getting in the way was a bonus. This is especially true as social power relationships are minimized and the playing field is equalized for all participants. The absence of verbal and visual cues leads to this. These findings were quickly countered with examples of friendship, love, and rewarding exchanges in social communities online. What became quickly obvious is that an unknown variable was affecting online exchanges. Speed appears to be one of these variables, as long term studies of CMC interactions show that eventually users convey all the same information as they might have face to face; it just took longer due to lack of a channel that directly transmits this information in person. Long-lasting groups have an anticipation of future interaction that promotes a sociality and feeling of cooperation. So rather than it being CMC that causes a lack of sociality, it is other features that do so. Comparing synchronous and asynchronous CMC, the asynchronous communications tended to have more social aspects, as participants could communicate at their leisure, where the synchronous communications necessarily had to focus on the task at hand with a limited amount of time available. Even task-related asynchronous communication has the potential to be better, as communications can be more deliberate and thoughtful. CMC can be compared not only to face to face communication but written forms of communication. CMC is rarely impersonal, but when it is it is due to other factors than the CMC system itself. CMC is interpersonal when users have time to compare, discuss, and otherwise build relationships. It just needs time, as it happens slower than face to face communication. CMC is hyperpersonal when users manage relationships in a better way online than they might face to face. Providing limited channels to communicate allows users to select what to present and how without preconceived notions getting in the way.
Wiley, D. (2006). Secretary of Education’s Commission on the Future of Higher Education; Panel on Innovative Teaching and Learning Strategies, February 2-3 2006. Accessed September 11, 2007 from http://www.ed.gov/about/bdscomm/list/hiedfuture/3rd-meeting/wiley.pdf
The world has moved from analog, closed, tethered, isolated, generic, and consumptive to an environment that is digital, open, mobile, connected, personal, and participative. Education tends to hold back; even a typical online course has moved to digital and mobile, but lags behind, keeping students isolated, using closed materials, generic content that is the same for everyone, and they must consume what is given to them rather than being allowed to create. Ensuring openness and transparency gives students and teachers the ability to be connected, personal, and creative. Without openness the classroom remains as it always has.
Xin, C. (2012). A Critique of the Community of Inquiry Framework. Journal of Distance Education 26(1).
The CoI refers to three key elements: cognitive presence, social presence, and teaching presence. As those aspects interact, learning takes place. Much of the research, however, is focused on the three pieces individually, not necessarily how they work together. The author claims that the CoI framework could actually apply in any educational environment, that it is not specifically suited to online asynchronous communication. The aspects are really just various functions on a continuum, just like colors in a rainbow, and while it serves a purpose to separate them, in fact many interactions serve more than one purpose. The author points out the concept of moderating as a function that communicates in a way that combines cognitive and social aspects. The moderating function includes providing context, monitoring conversation, summarizing conversations, and ensuring communication links are not broken. The author points out confusing of the term presence; the only way to show presence online is to communicate. You can’t just lurk. The CoI framework actually focuses on ability to perform different types of tasks, but if those functions are not carried out, presence is not displayed. Thus the social presence construct is supportive and implied. There is also ambiguity regarding whether social presence measures an action or a result. Regarding the teaching presence construct, many of the teaching presence indicators overlap with the moderating function. The process of creating orderly discussion out of chaos is the same process that constructs knowledge. The key is that communication must be consistently and intentionally produced by student and teacher. A face to face class is successful if students come and leave on time. An online discussion has no arbitrary beginning and end, thus a successful conversation online requires work from participants to keep it going.
Most traditional and online courses use a group-pacing model, but self-paced models give students more access and control. Given the increased control self-pacing provides, there is a corresponding lower social interaction and higher rate of attrition. Varying backgrounds of students do correlate with persistence, but the design of course materials and support provided to students is all that can be adjusted by the school/instructor. Social networking and other communication technologies allow us provide more learning support and interaction. Social software should provide sociability, a sense of trust and belonging; interaction, how connected students are with teachers and peers; and peer collaboration, constructivist learning with cohorts or informal groups. Many students are interested in interacting with peers, so course technology design should make this easier to do. Connectivist learning models have many of the answers. Survey of student interest in social technologies revealed a variety of responses. They are largely confident in using the internet but have only moderate exposure to many social media tools, with older students being less familiar than younger students. About half the students are interested in interacting with other students, and half are not, thus online collaborative activities should be compelling but not required.
Arbaugh, J.B. (2008) Does the community of inquiry framework predict outcomes in online MBA courses? International Review of Research in Open and Distance Learning 9(2).
The CoI framework describes teaching presence, social presence, and cognitive presence. Teaching presence includes course design and organization, direct instruction, and instructor facilitation. Social presence includes group cohesion, open communication, and affective expression. Cognitive presence includes a process where a problem is identified, explored, meaning constructed, and then the problem is solved. Study findings showed that the model accounted for 54% of the variance in student learning, with teaching presence and cognitive presence the strongest predictors of success. Social presence was a smaller predictor but still necessary to support the other two. Simpler or more familiar learning environments should produce higher cognitive gains (CLT).
de Souza, C. S., & Preece, J. (2004). A framework for analyzing and understanding online communities. Interacting with Computers, 16(3), 579-610.
This article points out two components by which an online community can be assessed: sociability (people, purposes, and policies) and usability (software). In their framework, these two components have to be aligned to produce success. Any community (whether online, offline, or a hybrid) will have sociability factors that change as the people (or purposes or policies) in the community change. For any online community, the software has to work with those people, purposes, and policies. They continue on to discuss Semiotics and HCI and how communication takes place among users and designers. The important part is that everyone is communicating all the time, but the message doesn't always get across how we expect it.
Gagne, R.M., Wager, W.W., Golas, K.C., & Keller, J.M. (2005). Online learning. In Principles of Instructional Design (5th Edition). Belmont, CA: Wadsworth.
While many instructional design theories related to offline computer-based instruction also apply to online learning, the networked nature of online learning means that there are additional affordances for students and teachers. Students can be located anywhere around the world and still participate with others and costs for delivering instruction are lower. Specialized software is not needed, as everything can be accessed through a web browser, content is delivered more efficiently, courses are flexible in allowing both self-paced and group-paced activities, updating content is instant, web analytics provide robust tracking of behaviors, and interactions among others can be increased. However, there may also be issues if students are overwhelmed with the amount of information without a clear path to follow through it all, and if social connections are not cultivated, individuals can be left on their own. If learners are required to access materials synchronously, that may cause scheduling conflicts; on the other hand, if learners are required to access materials on their own personal time, they may be unsatisfied with required training taking away from personal activities. Computer literacy is an important consideration. Learning management systems and other collaborative environments can support group work and constructivist teaching methods. Collaboration can be synchronous or asynchronous and text- or multimedia-based. Blended learning, with a combination of online and face to face activities is also an option.
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105.
The Community of Inquiry framework refers to three major components - cognitive presence, social presence, and teaching presence. Teaching presence is getting the conversation started and keeping it focused, cognitive presence is the desire to exchange ideas, and social presence is how well everyone gets along.
Lowenthal, P.R. (2010). The evolution and influence of social presence theory on online learning. In Online Learning and Adult Education. Hershey, PA: Information Science Reference.
The internet can be social and bring people together or separate and isolate them. It can also cause addiction and dependence. By studying the sociality of a network, we can better understand and deal with the negative issues while promoting the positives. Early research in computer mediated communication (CMC) showed that it did some things well and other things not as well. The theory of social presence was developed to help determine the quality of communication between people. Mediums with a high degree of social presence are considered warm and personal. The meaning surrounding a CMC message depends highly on context, thus initial research in primarily business environments made educational researchers and practitioners wary of trying out CMC, although those fears have been generally unfounded. Additional research in social presence theory has supported the principle that the extent to which a given aspect of a communication medium (visual, audio, etc.) is important depends on the task for which it is being used. Media richness is the extent to which a communication carries understanding and data. While some cues that promote richness are filtered out in CMC, users of the system find ways to convey that same richness, such as a winking emoticon where a physical wink is not possible. Future research in this area will move from online learning to the blurring of boundaries between the classroom and personal communications in social media.
McLuhan, M. (1964). The medium is the message. In Understanding Media: The Extensions of Man. New York: Signet.
The personal and social influences of any medium (or technology) can be positive or negative. Technologies shape us and affect our interactions. The example given is that the railway sped up expansion and growth of society, leading new cities and types of work, where the expansion of the airplane undoes the work the railroad did and builds different types of cities and work. The technology of the light bulb allows certain activities to take place, which could not otherwise, thus those activities are the message inherent in that technology. “The message, it seemed, was the content, as people used to ask what a painting was about. Yet they never thought to ask what a melody was about, nor what a house or a dress was about.” The content of the message often distracts from the real substance which should be the medium and the structural changes it makes in us and in our society.
Vonderwell, S. (2002). An examination of asynchronous communication experiences and perspectives of students in an online course: A case study. The Internet and Higher Education, 6, 77-90.
This study looked at how asynchronous communication affects student learning and whether or not it enhances it. Interaction among students and between students and teachers is an important part of the learning process. Pulling apart and putting back together beliefs and knowledge help promote collective knowledge building. Introverts tend to participate more in CMC environments, while extroverts tend to participate less. In the study, an online technology course for education majors, the students appreciated the opportunity to interact electronically with the instructor and that they did not worry as much about what other students thought of them as they do in a traditional classroom. It seemed easier to ignore questions in the online classroom that would have to be immediately answered in a face to face environment, so some negative aspects frustrated some students. Response time by the instructor tended to slow down toward the end of the semester, where responses and grading were fast at the beginning of the semester. Some groups had a hard time collecting information needed to write group papers, while others felt that they learned from each other and collaborated well. Students attempted to ensure their writing was more clear to avoid miscommunication issues. A balance should exist between quick instructor feedback and giving students time to carry out their own inquiry.
Walther, J.B. (1996) Computer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction. Communication Research 23(1).
The initial line of research into CMC showed that the medium is not rich or personal enough to convey important interpersonal communication or effective task-related communications. So if it’s not good for tasks or socializing, what is it used for? In early studies with zero history groups using CMC, it was effective as a task-centered communication mechanism. Low social interaction in these zero history groups led CMC to be pegged as depersonalized. For groups with an impersonal focus, this is actually a good thing. Task orientation without affect getting in the way was a bonus. This is especially true as social power relationships are minimized and the playing field is equalized for all participants. The absence of verbal and visual cues leads to this. These findings were quickly countered with examples of friendship, love, and rewarding exchanges in social communities online. What became quickly obvious is that an unknown variable was affecting online exchanges. Speed appears to be one of these variables, as long term studies of CMC interactions show that eventually users convey all the same information as they might have face to face; it just took longer due to lack of a channel that directly transmits this information in person. Long-lasting groups have an anticipation of future interaction that promotes a sociality and feeling of cooperation. So rather than it being CMC that causes a lack of sociality, it is other features that do so. Comparing synchronous and asynchronous CMC, the asynchronous communications tended to have more social aspects, as participants could communicate at their leisure, where the synchronous communications necessarily had to focus on the task at hand with a limited amount of time available. Even task-related asynchronous communication has the potential to be better, as communications can be more deliberate and thoughtful. CMC can be compared not only to face to face communication but written forms of communication. CMC is rarely impersonal, but when it is it is due to other factors than the CMC system itself. CMC is interpersonal when users have time to compare, discuss, and otherwise build relationships. It just needs time, as it happens slower than face to face communication. CMC is hyperpersonal when users manage relationships in a better way online than they might face to face. Providing limited channels to communicate allows users to select what to present and how without preconceived notions getting in the way.
Wiley, D. (2006). Secretary of Education’s Commission on the Future of Higher Education; Panel on Innovative Teaching and Learning Strategies, February 2-3 2006. Accessed September 11, 2007 from http://www.ed.gov/about/bdscomm/list/hiedfuture/3rd-meeting/wiley.pdf
The world has moved from analog, closed, tethered, isolated, generic, and consumptive to an environment that is digital, open, mobile, connected, personal, and participative. Education tends to hold back; even a typical online course has moved to digital and mobile, but lags behind, keeping students isolated, using closed materials, generic content that is the same for everyone, and they must consume what is given to them rather than being allowed to create. Ensuring openness and transparency gives students and teachers the ability to be connected, personal, and creative. Without openness the classroom remains as it always has.
Xin, C. (2012). A Critique of the Community of Inquiry Framework. Journal of Distance Education 26(1).
The CoI refers to three key elements: cognitive presence, social presence, and teaching presence. As those aspects interact, learning takes place. Much of the research, however, is focused on the three pieces individually, not necessarily how they work together. The author claims that the CoI framework could actually apply in any educational environment, that it is not specifically suited to online asynchronous communication. The aspects are really just various functions on a continuum, just like colors in a rainbow, and while it serves a purpose to separate them, in fact many interactions serve more than one purpose. The author points out the concept of moderating as a function that communicates in a way that combines cognitive and social aspects. The moderating function includes providing context, monitoring conversation, summarizing conversations, and ensuring communication links are not broken. The author points out confusing of the term presence; the only way to show presence online is to communicate. You can’t just lurk. The CoI framework actually focuses on ability to perform different types of tasks, but if those functions are not carried out, presence is not displayed. Thus the social presence construct is supportive and implied. There is also ambiguity regarding whether social presence measures an action or a result. Regarding the teaching presence construct, many of the teaching presence indicators overlap with the moderating function. The process of creating orderly discussion out of chaos is the same process that constructs knowledge. The key is that communication must be consistently and intentionally produced by student and teacher. A face to face class is successful if students come and leave on time. An online discussion has no arbitrary beginning and end, thus a successful conversation online requires work from participants to keep it going.
Tuesday, October 15, 2013
Andragogy - An Annotated Bibliography
Ausburn, L.J. (2004). Course design elements most valued by adult learners in blended online education environments: An American perspective. Educational Media International 41(4).
Part time adult learners make up over half of the higher education market. The for-profit schools have grown rapidly due to their focus on serving these nontraditional students. Established principles for meeting the needs of adults include understanding the purpose of the learning, how it will be applied, relevance to experience, and self-direction. Studies show that previous experience in non-traditional education environments and gender affect attitude and behavior in distance learning. Interactive course designs and promote communication and bonding in learning communities is desired. The study looked at gender, technology skills, prior experience in distance learning, prior experience with self-directed learning, and the features of the course they thought were most helpful. The ATLAS instrument used in the study identified three main types of learners: navigators (results-oriented learners looking for efficient navigation of the course), problem solvers (critical thinkers who analyze a problem deeply before solving it), and engagers (passionate learners with high involvement). The most important course features to learners dealt with the structure and guidance of the course. Next comes the instructional content. The next was convenience factors that streamlined common tasks. And last was communication features. In terms of content, the most important instructional goals related to individualization, self-directed learning, variety of activity types, and communication/interaction. Some features showed up in different orders for each of the student types.
Bergeven, P. (1967). Concepts to implement the education of adults. In A philosophy for adult education. New York: The Seabury Press.
Adults can learn. Some adults may not participate in lifelong learning due to lack of organization, discipline, or a desire to change. While everyone is limited to some extent by age, mental capacity, or otherwise, everyone can break through these differences and excuses to learn. Adults and children have different educational processes. Some teachers are hired for adult education because they are good at child education, but understanding the differences between adults and children will help teachers of adults be more effective. Adults are more focused on creating/maintaining family relationships, raising children, working, keeping up the home, and social responsibilities. Physical ailments make it more difficult to learn as one gets older, and changing one’s opinions and mindset is also more difficult as one ages and becomes more set in their ways. They do, however, have longer attention spans than children, as well as experiences to draw from. Existing institutions are effective channels. Schools, libraries, churches, hospitals, and other organizations should all take on the responsibility to push for adult learning. Adult education programs should be indigenous. Education should be natural, built around real problems they are experiencing and in a familiar environment. Expectations should be reconciled. Each person has something different they are looking to learn from a class or a lesson, including the teacher. Freedom is important in learning. Learners should be able to choose the content, leaders, time, and place of a program of learning. Too much freedom can be destructive if not equally shared across all participants. Goals are vital. Realistic, specific goals should help shape learning (SMART). Learning how to learn is helpful. Metacognition is one of the most important trainings that can take place. Needs must be considered. Both the learner and the organization sponsoring training have needs that need to be aligned. Problem-centered learning is basic. Adults understand a need to learn something if they can see a problem it helps solve. Resources should be appropriate. Technology, format of content, and other appropriate equipment should be utilized. A programming cycle should be a cooperative experience. Participants should help in the planning, organizing, conducting and evaluating of an educational program.
Blaschke, L.M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning. The International Review of Research in Open and Distance Learning 13(1).
Self-directed and self-determined learning is becoming more important for our increasingly complex world. Distance education is especially suited to this self-directed learning in the way of heutagogy. The article begins with andragogy as the basis for showing the need to help learners become self-directed. They need to learn to diagnose their own needs, set goals, identify appropriate resources and strategies, and evaluate learning outcomes. The educator is a mentor. With heutagogy, the processes are similar, but learners take an even greater role. Double loop learning is a key strategy, where learners not only determine how their actions solve a problem but also how the problem solving process influences the learner’s beliefs. Learners become both capable and competent. Due to the technologies we have available to us, the experience and maturity of adult students, and the built-in autonomy of distance education environment, heutagogy seems a natural fit. Social media technologies support education going mobile and generating their own content. Heutagogy certainly causes concerns when it comes to accreditation, but some higher educaiotn institutions have found success handing over more control to students. Aspects of a heutagogical learning environment include learner defined learning contracts, flexible curriculum, learner-directed questions, flexible assessment, and a holistic approach to being a lifelong learner.
Christensen, C.M. & Eyring, H.J. (2011). Change and the indispensable university. In The Innovative University. San Francisco, CA: Jossey-Bass.
There is great demand for higher education, but also great need for change. Higher education has grown in the past due to innovation, and it needs that again. Decreasing prices and better service to students are some of the keys, in addition to continuing to support traditional research roles. By growing what it means to be a university, rising tides lift all ships, everyone can win. Schools should be ranked based on their outputs, not their inputs, so traditional ranking systems will (or should) become relics of the past. Schools with different missions shouldn’t compete against each other but for their own definition of success.
Dewey, J. 1998. Experience and education. (60th anniversary ed.) West Lafayette, IN: Kappa Delta Pi.
Different schools of thought exist around what education is – a natural growth of one’s self or a change pushed at someone from the outside. While genuine education may come from experience, that does not mean all experience are educational. Every experience we have modifies us and affects the quality of subsequent experiences (good or bad). Factors of both the internal thoughts of the learner and the objective goals of the teacher need to be considered. Traditional educators create a place for students to learn without considering the background of learners; just because a condition is effective for one group at one time does not mean it will work again. Education can be used to improve desires and impulses, but that should be done by helping the individual reflect and make self-judgment rather than impose judgment from the outside.
Freire, P. (1970). Chapter 2. In Pedagogy of the oppressed. New York: Herder and Herder.
The banking model of education consists of the teacher expounding to and filling up passive students with detached words. As empty receptacles, they lose their natural creativity. The solution to this problematic approach to oppressing learners is to transform the educational structure to allow students to be themselves. Rather than wait and hope that students will break the mold on their own, it is the calling of the revolutionary educator to help students become partners in their education. Problems to be solved liberate the students. Teachers learn and students teach in the new liberated model.
Gagne, R.M., Wager, W.W., Golas, K.C., & Keller, J.M. (2005). The Learner. In Principles of Instructional Design (5th Edition). Belmont, CA: Wadsworth.
Learners have many of the same characteristics, but vary in their abilities for each characteristic. That is, if all the students can read, perhaps one reads well and another poorly. Students can learn intellectual skills or procedures, cognitive strategies or productions, verbal information or facts, attitudes, and motor skills. Learning the above is affected by factors of motivation (ARCS), developmental and social factors, and individual differences. The ability to process concepts to build schemas and store in memory is one that differs from learner to learner.
Gibbings, P., Lidstone, J., & Bruce, C. (2010). How do student attributes influence the way students experience problem-based learning in virtual space? Australasian Journal of Engineering Education, 16(1), 69–80.
Study of engineering students in a PBL engineering course found that students expected the class to help them learn as they started the class, and that after the PBL class, students found that a PBL course really did help them learn better how to learn.
Isenberg, S. (2007). Background. In Applying andragogical princples to internet learning. Youngstown, New York: Cambria Press.
There is a lot of talk about andragogy and about the Internet, but little about both together. The Socratic method promotes probing questions from the learner and self-assessment to help the student construct knowledge. The field of mass communication has shown that diffusion of knowledge needs to happen systematically, with clear scaffolding. The Internet allows for enhanced systematic diffusion of knowledge. The Internet allows for customized curriculum for each learner, both helping the learner with the content and with his or her own metacognition. The whole-part-whole model can be facilitated online, by showing worked examples, helping the learner practice the parts, and then practice all the skills in one immersive case. Preassessments can automatically guide students to areas where they are deficient. Self-learning does not have to be alone learning. Virtual groups can be provided to allow for important interactions with peers while still being self-paced. Internet learning requires learners and teachers to be engaged and needs to be made easier to use.
Kenyon, C., & Hase, S. (2001). Moving from andragogy to heutagogy in vocational education. Retrieved April 2, 2013 from http://www.avetra.org.au/abstracts_and_papers_2001/Hase-Kenyon_full.pdf
Traditional education is pedagogical, where the teacher is in control of everything. Andragogy improves educational methods but still has a student/teacher dichotomy to deal with. Heutagogy provides a movement even further in self-directed learning. The key is a holistic approach to build independent capability. Heutagogy is most effective in a flexible environment, where resources are provided without too much direction as to the specific content that needs to be studied. The ideal learning environment challenges learners to ask deep questions without getting in the way too much.
Knowles, M.S., Holton, E.F., & Swanson, R.A. (2011). A theory of adult learning: Andragogy. In The adult learner: The definitive classic in adult education and human resource development (7th ed.). Burlington, MA: Butterworth-Heinemann.
The most famous teachers in history worked with adults, not children. They used processes of mental inquiry rather than passive reception of transmissions. Learners delved into cases and dialogue. They challenged and confronted and defended. Proponents of traditional pedagogical methods will claim that adults are uninterested in learning; if they were interested they would make it happen. Of course, the key is to engage adults and make them aware of the learning they have available to them. Adults are motivated by satisfying needs, centered around their life, have much experience, work best when self-directed, and have unique needs.
Kopcha, T. J., & Sullivan, H. (2008). Learner preferences and prior knowledge in learner-controlled computer-based instruction. Educational Technology Research and Development, 56(3), 265-286.
Studies have shown that giving students control over their learning motivates them. Computer-based instruction is an effective method for maximizing learner control. In the study, students were given a computer based instruction program for teaching math, with varying levels of control over the program. Students were broken out by preference for control and prior knowledge. They found that when students had high prior knowledge, they preferred having control over their learning, while students with low prior knowledge preferred high teacher control. This may be because lower performing students simply want to get through the course quickly or are not confident in their abilities, but preference for control had no significant interaction.
Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43-59.
Merrill’s model builds on many existing instructional design models. The key principles are that learning should deal with real-world problems, activate prior knowledge, demonstrate new knowledge, and apply and integrate new learning. Problems should become progressively more complex. Both examples and non-examples should be demonstrated, and relevant media should be used. Coaching should be gradually withdrawn as the learner’s ability ramps up.
Tomei, L.A. (2010). A theoretical model for designing online education in support of lifelong learning. In Online Learning and Adult Education. Hershey, PA: Information Science Reference.
Web-based courses increase the potential for students to collaborate and share information with others, potentially leading to higher order skills. The traditional ADDIE model has stood over time as a solid yet generic process for designing instruction in a straight forward way. Wiggins & McTighe’s Backwards Design model and others have refocused ADDIE to meet the needs of the educational environment. Behaviorists, cognitivists, and humanists each describe different theories about how we learn. Just as learning taxonomies, such as Bloom’s categorize the types of knowledge that can be learned, distance learners need a taxonomy of skills for dealing with technology: literacy, communication, decision-making, infusion, integration, and technology. Learning materials, whether print, video, data, or otherwise provide tools and technologies that may be swapped out with each other to meet learner preferences. Adult learners prefer collaborative and realistic learning experiences. Distance education students use various synchronous and asynchronous tools to help them achieve a similar collaborative environment. Adults may be assessed by having them build higher order responses on Bloom’s taxonomy, while built-in analytics in various forms can help the learning environment assess the student with his or her input.
Weigel, M., James, C., Gardner, H. (2009). Learning: Peering backward and looking forward in the digital era. International Journal of Learning and Media 1(1).
While methods of education have changed greatly over the past decades or even centuries, the need for relevance has never been so important. Current youth use digital technologies extensively, yet understand little about how they work or how to create the technology. The rate of change with new technology means that even once they get a technology down, it won’t stay around that way for very long. Constructivism, with its increased engagement and investment of the learner and decreased control of the teacher, modern learning theories fit the Internet’s potential for deep exploration. But for that to happen, they need to have solid skills to navigate the technology, including the underlying biases and structures that affect its use. The number and vast difference in quality of resources found online serve to make the process difficult for the non-information-literate student. The social aspect, which allows for back channel communication and instant sharing of ideas serve to add to the crowdsourcing culture, which helps construct new knowledge effectively with a risk of drowning out dissent. Moderate changes to schooling have changed little about how students learn, but the technologies now available, in combination with the accepted learning theories such as andragogy are pushing towards a radical shift in education.
Part time adult learners make up over half of the higher education market. The for-profit schools have grown rapidly due to their focus on serving these nontraditional students. Established principles for meeting the needs of adults include understanding the purpose of the learning, how it will be applied, relevance to experience, and self-direction. Studies show that previous experience in non-traditional education environments and gender affect attitude and behavior in distance learning. Interactive course designs and promote communication and bonding in learning communities is desired. The study looked at gender, technology skills, prior experience in distance learning, prior experience with self-directed learning, and the features of the course they thought were most helpful. The ATLAS instrument used in the study identified three main types of learners: navigators (results-oriented learners looking for efficient navigation of the course), problem solvers (critical thinkers who analyze a problem deeply before solving it), and engagers (passionate learners with high involvement). The most important course features to learners dealt with the structure and guidance of the course. Next comes the instructional content. The next was convenience factors that streamlined common tasks. And last was communication features. In terms of content, the most important instructional goals related to individualization, self-directed learning, variety of activity types, and communication/interaction. Some features showed up in different orders for each of the student types.
Bergeven, P. (1967). Concepts to implement the education of adults. In A philosophy for adult education. New York: The Seabury Press.
Adults can learn. Some adults may not participate in lifelong learning due to lack of organization, discipline, or a desire to change. While everyone is limited to some extent by age, mental capacity, or otherwise, everyone can break through these differences and excuses to learn. Adults and children have different educational processes. Some teachers are hired for adult education because they are good at child education, but understanding the differences between adults and children will help teachers of adults be more effective. Adults are more focused on creating/maintaining family relationships, raising children, working, keeping up the home, and social responsibilities. Physical ailments make it more difficult to learn as one gets older, and changing one’s opinions and mindset is also more difficult as one ages and becomes more set in their ways. They do, however, have longer attention spans than children, as well as experiences to draw from. Existing institutions are effective channels. Schools, libraries, churches, hospitals, and other organizations should all take on the responsibility to push for adult learning. Adult education programs should be indigenous. Education should be natural, built around real problems they are experiencing and in a familiar environment. Expectations should be reconciled. Each person has something different they are looking to learn from a class or a lesson, including the teacher. Freedom is important in learning. Learners should be able to choose the content, leaders, time, and place of a program of learning. Too much freedom can be destructive if not equally shared across all participants. Goals are vital. Realistic, specific goals should help shape learning (SMART). Learning how to learn is helpful. Metacognition is one of the most important trainings that can take place. Needs must be considered. Both the learner and the organization sponsoring training have needs that need to be aligned. Problem-centered learning is basic. Adults understand a need to learn something if they can see a problem it helps solve. Resources should be appropriate. Technology, format of content, and other appropriate equipment should be utilized. A programming cycle should be a cooperative experience. Participants should help in the planning, organizing, conducting and evaluating of an educational program.
Blaschke, L.M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning. The International Review of Research in Open and Distance Learning 13(1).
Self-directed and self-determined learning is becoming more important for our increasingly complex world. Distance education is especially suited to this self-directed learning in the way of heutagogy. The article begins with andragogy as the basis for showing the need to help learners become self-directed. They need to learn to diagnose their own needs, set goals, identify appropriate resources and strategies, and evaluate learning outcomes. The educator is a mentor. With heutagogy, the processes are similar, but learners take an even greater role. Double loop learning is a key strategy, where learners not only determine how their actions solve a problem but also how the problem solving process influences the learner’s beliefs. Learners become both capable and competent. Due to the technologies we have available to us, the experience and maturity of adult students, and the built-in autonomy of distance education environment, heutagogy seems a natural fit. Social media technologies support education going mobile and generating their own content. Heutagogy certainly causes concerns when it comes to accreditation, but some higher educaiotn institutions have found success handing over more control to students. Aspects of a heutagogical learning environment include learner defined learning contracts, flexible curriculum, learner-directed questions, flexible assessment, and a holistic approach to being a lifelong learner.
Christensen, C.M. & Eyring, H.J. (2011). Change and the indispensable university. In The Innovative University. San Francisco, CA: Jossey-Bass.
There is great demand for higher education, but also great need for change. Higher education has grown in the past due to innovation, and it needs that again. Decreasing prices and better service to students are some of the keys, in addition to continuing to support traditional research roles. By growing what it means to be a university, rising tides lift all ships, everyone can win. Schools should be ranked based on their outputs, not their inputs, so traditional ranking systems will (or should) become relics of the past. Schools with different missions shouldn’t compete against each other but for their own definition of success.
Dewey, J. 1998. Experience and education. (60th anniversary ed.) West Lafayette, IN: Kappa Delta Pi.
Different schools of thought exist around what education is – a natural growth of one’s self or a change pushed at someone from the outside. While genuine education may come from experience, that does not mean all experience are educational. Every experience we have modifies us and affects the quality of subsequent experiences (good or bad). Factors of both the internal thoughts of the learner and the objective goals of the teacher need to be considered. Traditional educators create a place for students to learn without considering the background of learners; just because a condition is effective for one group at one time does not mean it will work again. Education can be used to improve desires and impulses, but that should be done by helping the individual reflect and make self-judgment rather than impose judgment from the outside.
Freire, P. (1970). Chapter 2. In Pedagogy of the oppressed. New York: Herder and Herder.
The banking model of education consists of the teacher expounding to and filling up passive students with detached words. As empty receptacles, they lose their natural creativity. The solution to this problematic approach to oppressing learners is to transform the educational structure to allow students to be themselves. Rather than wait and hope that students will break the mold on their own, it is the calling of the revolutionary educator to help students become partners in their education. Problems to be solved liberate the students. Teachers learn and students teach in the new liberated model.
Gagne, R.M., Wager, W.W., Golas, K.C., & Keller, J.M. (2005). The Learner. In Principles of Instructional Design (5th Edition). Belmont, CA: Wadsworth.
Learners have many of the same characteristics, but vary in their abilities for each characteristic. That is, if all the students can read, perhaps one reads well and another poorly. Students can learn intellectual skills or procedures, cognitive strategies or productions, verbal information or facts, attitudes, and motor skills. Learning the above is affected by factors of motivation (ARCS), developmental and social factors, and individual differences. The ability to process concepts to build schemas and store in memory is one that differs from learner to learner.
Gibbings, P., Lidstone, J., & Bruce, C. (2010). How do student attributes influence the way students experience problem-based learning in virtual space? Australasian Journal of Engineering Education, 16(1), 69–80.
Study of engineering students in a PBL engineering course found that students expected the class to help them learn as they started the class, and that after the PBL class, students found that a PBL course really did help them learn better how to learn.
Isenberg, S. (2007). Background. In Applying andragogical princples to internet learning. Youngstown, New York: Cambria Press.
There is a lot of talk about andragogy and about the Internet, but little about both together. The Socratic method promotes probing questions from the learner and self-assessment to help the student construct knowledge. The field of mass communication has shown that diffusion of knowledge needs to happen systematically, with clear scaffolding. The Internet allows for enhanced systematic diffusion of knowledge. The Internet allows for customized curriculum for each learner, both helping the learner with the content and with his or her own metacognition. The whole-part-whole model can be facilitated online, by showing worked examples, helping the learner practice the parts, and then practice all the skills in one immersive case. Preassessments can automatically guide students to areas where they are deficient. Self-learning does not have to be alone learning. Virtual groups can be provided to allow for important interactions with peers while still being self-paced. Internet learning requires learners and teachers to be engaged and needs to be made easier to use.
Kenyon, C., & Hase, S. (2001). Moving from andragogy to heutagogy in vocational education. Retrieved April 2, 2013 from http://www.avetra.org.au/abstracts_and_papers_2001/Hase-Kenyon_full.pdf
Traditional education is pedagogical, where the teacher is in control of everything. Andragogy improves educational methods but still has a student/teacher dichotomy to deal with. Heutagogy provides a movement even further in self-directed learning. The key is a holistic approach to build independent capability. Heutagogy is most effective in a flexible environment, where resources are provided without too much direction as to the specific content that needs to be studied. The ideal learning environment challenges learners to ask deep questions without getting in the way too much.
Knowles, M.S., Holton, E.F., & Swanson, R.A. (2011). A theory of adult learning: Andragogy. In The adult learner: The definitive classic in adult education and human resource development (7th ed.). Burlington, MA: Butterworth-Heinemann.
The most famous teachers in history worked with adults, not children. They used processes of mental inquiry rather than passive reception of transmissions. Learners delved into cases and dialogue. They challenged and confronted and defended. Proponents of traditional pedagogical methods will claim that adults are uninterested in learning; if they were interested they would make it happen. Of course, the key is to engage adults and make them aware of the learning they have available to them. Adults are motivated by satisfying needs, centered around their life, have much experience, work best when self-directed, and have unique needs.
Kopcha, T. J., & Sullivan, H. (2008). Learner preferences and prior knowledge in learner-controlled computer-based instruction. Educational Technology Research and Development, 56(3), 265-286.
Studies have shown that giving students control over their learning motivates them. Computer-based instruction is an effective method for maximizing learner control. In the study, students were given a computer based instruction program for teaching math, with varying levels of control over the program. Students were broken out by preference for control and prior knowledge. They found that when students had high prior knowledge, they preferred having control over their learning, while students with low prior knowledge preferred high teacher control. This may be because lower performing students simply want to get through the course quickly or are not confident in their abilities, but preference for control had no significant interaction.
Merrill, M. D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43-59.
Merrill’s model builds on many existing instructional design models. The key principles are that learning should deal with real-world problems, activate prior knowledge, demonstrate new knowledge, and apply and integrate new learning. Problems should become progressively more complex. Both examples and non-examples should be demonstrated, and relevant media should be used. Coaching should be gradually withdrawn as the learner’s ability ramps up.
Tomei, L.A. (2010). A theoretical model for designing online education in support of lifelong learning. In Online Learning and Adult Education. Hershey, PA: Information Science Reference.
Web-based courses increase the potential for students to collaborate and share information with others, potentially leading to higher order skills. The traditional ADDIE model has stood over time as a solid yet generic process for designing instruction in a straight forward way. Wiggins & McTighe’s Backwards Design model and others have refocused ADDIE to meet the needs of the educational environment. Behaviorists, cognitivists, and humanists each describe different theories about how we learn. Just as learning taxonomies, such as Bloom’s categorize the types of knowledge that can be learned, distance learners need a taxonomy of skills for dealing with technology: literacy, communication, decision-making, infusion, integration, and technology. Learning materials, whether print, video, data, or otherwise provide tools and technologies that may be swapped out with each other to meet learner preferences. Adult learners prefer collaborative and realistic learning experiences. Distance education students use various synchronous and asynchronous tools to help them achieve a similar collaborative environment. Adults may be assessed by having them build higher order responses on Bloom’s taxonomy, while built-in analytics in various forms can help the learning environment assess the student with his or her input.
Weigel, M., James, C., Gardner, H. (2009). Learning: Peering backward and looking forward in the digital era. International Journal of Learning and Media 1(1).
While methods of education have changed greatly over the past decades or even centuries, the need for relevance has never been so important. Current youth use digital technologies extensively, yet understand little about how they work or how to create the technology. The rate of change with new technology means that even once they get a technology down, it won’t stay around that way for very long. Constructivism, with its increased engagement and investment of the learner and decreased control of the teacher, modern learning theories fit the Internet’s potential for deep exploration. But for that to happen, they need to have solid skills to navigate the technology, including the underlying biases and structures that affect its use. The number and vast difference in quality of resources found online serve to make the process difficult for the non-information-literate student. The social aspect, which allows for back channel communication and instant sharing of ideas serve to add to the crowdsourcing culture, which helps construct new knowledge effectively with a risk of drowning out dissent. Moderate changes to schooling have changed little about how students learn, but the technologies now available, in combination with the accepted learning theories such as andragogy are pushing towards a radical shift in education.
Monday, September 30, 2013
Why Wikipedia is Awesome
From the Wikipedia article for the song Fishin' in the Dark by Nitty Gritty Dirt Band:
The premise of the song is a couple contemplating a late night fishing expedition. Specifically, the adventurers plan to make their way to a river and chart constellations in the full moon light. In the song, the tentative date for this excursion is set in the late spring to early summer.This is a great example of a pseudo-scientific neutral point of view.
Wednesday, November 21, 2012
Origin of the Universe
I occasionally ponder on the origins of the earth. When I was younger I could make my brain hurt trying to think of how there could have always been something that existed somewhere and how there is no end to anything, yet everything we know is that there is always a start and end. So how did we start if there was nothing there to start it? I don't worry about that anymore. Why? A scripture in the bible solved it for me, and not the one in 2 Peter 3 about a day for the Lord being 1000 years for us, but one in Revelation of all places.
Revelation 10:5-7
5 And the angel which I saw stand upon the sea and upon the earth lifted up his hand to heaven,
6 And sware by him that liveth for ever and ever, who created heaven, and the things that therein are, and the earth, and the things that therein are, and the sea, and the things which are therein, that there should be time no longer:
7 But in the days of the voice of the seventh angel, when he shall begin to sound, the mystery of God should be finished, as he hath declared to his servants the prophets.
So time will be turned off. It's a temporary constraint to our understanding. I don't understand what the big picture is, but I know time isn't permanent. At least I believe it doesn't, to the extent that it no longer gives me headaches.
It then makes me wonder when I see an article like this one that purports to explain Why Marco Rubio Needs To Know That The Earth Is Billions Of Years Old. Go read it if you want. The author, who is a technology writer for Forbes (not a theologian or a scientist), calls out Marco Rubio for answering truthfully that he doesn't know how old the earth is. Rubio states that he is neither a theologian or a scientist, and none of those experts can agree, so he's leaving the debate up to them.
The interesting point that is called out of the many that could have been is that if the earth isn't billions of years old, then all our DVD players will stop working, laser surgery will start failing, and our nuclear reactors will all blow up tomorrow (Are you paying attention, Mayans?) because everything we know about science is wrong.
Awkward pause.
Just a minute while I finish looking up the Wikipedia article on logical fallacies.
One of the most important ideas that I learned about in the multivariate statistics class I took in my PhD program (which I will get to blogging about in a couple years at the rate I'm going) is the principle of parsimony. The idea is that you go with the model that is the appropriate balance between simplicity and completeness, or you start with the simplest hypothesis, with the fewest number of assumptions, and work towards the more complex ones.
Thinking about all the elements that would have had to blow up just right to form new elements and align themselves somehow into what would become self-aware beings is a bit complex for me and the basis of another big headache. That's not to say I don't believe the earth is billions of years old, or that at least the materials used to create the earth are that old. I said before that it gives me comfort that time will be turned off at some point, but that doesn't mean that time doesn't still exist and play a role in our larger existence, just that it won't be limiting as it is now.
Foes of the religious will dismiss what I'm about to say as me dealing with my cognitive dissonance, but hear me out. This is the parsimonious model I've come up with that brings together my belief in the bible and the creation and in my understanding and trust of scientists. Matter in various shapes and forms has existed for a long time, billions of years or more even. The universe has existed for billions of years. God has existed for billions of years. If we read the biblical history literally, our earth was only created maybe 10,000 years ago, depending on when the clock started ticking. It was created from remnants of other worlds and placed with our solar system into the universe that already existed.
The laws of nature, such as how light works, gravitational pull, and chemistry, are constant. They haven't changed. Our DVDs will still work tomorrow. At least I hope the laws of physics will last long enough for me to see The Hobbit. We just have some billion year old recycled pieces of another planet that happened to have animals that were a lot scarier than the ones we have now. No, dinosaurs never walked this planet. They walked on another planet, a long time ago in a galaxy far, far away. Not reasonable? Which is more far-fetched? God recycled the dinosaur's planet in making ours or the earth and dinosaurs evolved from nothing, they were all killed from a meteor and resulting ash cloud, and then we evolved again from nothing? The scientists are guessing when they come up with their hypotheses about why the dinosaurs went away. They don't know. So why is my guess of a hypothesis any worse off than theirs? Which is more parsimonious?
I learned within the past year or two about dark matter. Granted I don't know a lot about it, but the basics of it is that there is something that exists in such a way that it increases the mass (and thus the gravitational pull) of galaxies but that cannot be seen. Wait, let's think about that. We know (or think we know) how gravity works. But something in the galaxy behaves in a way inconsistent with our understanding of gravity. So scientists hypothesize that there is a mysterious, invisible matter that accounts for 84% of the mass of the universe in order to make their previously held theory (gravity) continue to work. Now is a good time to refer back to the cognitive dissonance link in the "Foes of the religious..." paragraph above.
Understanding how the universe works seems to me to be a completely separate question from how we and the particular world we live on were created. They still don't know for sure where the moon came from. But we do know it's there and can predict its movements and measure its effects on the tides. We don't know where the dinosaurs came from or how they died, but we do know that their rotting flesh makes good fuel for powering our cars and heating our homes. Well, maybe oil comes from dinosaurs.
My pointing out the great deficiencies in the knowledge of scientists does not mean I think they are idiots but rather to point out that any of the things they say about religious folks could apply equally to them. I do respect scientists, and I believe that many useful discoveries can be made by investigating the origin of the universe, just as much as studying the operation of the universe. As of next month (December 2012), it will be 40 years since a man last stood on the moon, and I think we should go back.
More importantly, I think we should be more respectful of one another's ideas, because if I'm right, then both theologians and scientists are right. If I'm not right, chances are both of them are wrong with me as well, and neither has the standing to point out the flaws of the other.
Friday, November 4, 2011
Business Communication
Business Communication was a great class. I enjoyed it and still use things I learned in it. One great thing about it was that it was a night class taught once a week. I like the extended time less often, because it seems that you can get into deeper conversations given the extra time. I like night classes a lot more than early a.m. classes, since I'd rather stay up late than get up early.
The content of the course was useful. We learned a ton of grammar, which I really appreciated. My spelling and grammar have always been pretty good anyway, but there were things I learned in this class that I had never grasped before. I do purposely break certain rules for various reasons, but it always is for a reason when I do. I do occasionally start a sentence with "And" but almost exclusively on my blog and only for added emphasis. The unfortunate thing about this class is that they have since removed the grammar component. Given my AP and ACT scores, I'd have qualified to skip the grammar pretest and never had to learn any of the rules that I actually appreciate having learned in this class.
A couple examples:
When using conjunctions such as and, because, but, and or, you only put a comma before the conjunction if what follows the conjunction could stand alone as a complete sentence. I raked the leaves and emptied all the garbage cans. No comma since "emptied all the garbage cans" is not a complete sentence. I had previously always used a comma before a conjunction.
You can tack two complete, related sentences together with a semicolon and no conjunction. I will pick up dinner at Pizza Pie Cafe on my way home; they are having a sweet sale right now.
Something I'll always remember about this class was the overhead slides. No, the weird part isn't that the professor used overhead slides instead of computer-based slides, although that was a little strange, too. This was and is one of my favorite professors of all time but for whatever reason had a strange method for displaying the overheads: lay slide on overhead, turn on overhead, talk about slide, turn off overhead, lay slide on overhead, turn on overhead, etc. The overhead projector was turned on and off for every single slide. I remember joking with my classmates that any electricity savings from the two seconds it was turned off while picking up the next transparency off the pile would surely be lost by having the replace the worn out power switch before its time.
I also recall that the final paper for this class had to be cancelled. It was supposed to be a research paper, which we'd write with all our new-found communication skills. The problem was that when you do research, you have to get IRB approval, even if it's a simple survey to ask people about their parking behaviors, which ours was. IRB approval usually takes months, and while there are provisions for blanket IRB approval for class-based research projects in certain situations, it was too late for that process as well. Being the go-getters that we were (and since I was writing a paper about the same topic in my English class the same semester), we already had our research collected by the time the sad news was delivered. We ended up writing and turning in our paper anyway for extra credit. The extra credit should have been reversed, since we threw a bunch of screen beans into it, and that flies in the face of any kind of professional communication which we had been learning about. It's too bad I couldn't have taken some of that extra credit and applied it to the grade in my English class, for which I was using some of the same content.
The content of the course was useful. We learned a ton of grammar, which I really appreciated. My spelling and grammar have always been pretty good anyway, but there were things I learned in this class that I had never grasped before. I do purposely break certain rules for various reasons, but it always is for a reason when I do. I do occasionally start a sentence with "And" but almost exclusively on my blog and only for added emphasis. The unfortunate thing about this class is that they have since removed the grammar component. Given my AP and ACT scores, I'd have qualified to skip the grammar pretest and never had to learn any of the rules that I actually appreciate having learned in this class.
A couple examples:
When using conjunctions such as and, because, but, and or, you only put a comma before the conjunction if what follows the conjunction could stand alone as a complete sentence. I raked the leaves and emptied all the garbage cans. No comma since "emptied all the garbage cans" is not a complete sentence. I had previously always used a comma before a conjunction.
You can tack two complete, related sentences together with a semicolon and no conjunction. I will pick up dinner at Pizza Pie Cafe on my way home; they are having a sweet sale right now.
Something I'll always remember about this class was the overhead slides. No, the weird part isn't that the professor used overhead slides instead of computer-based slides, although that was a little strange, too. This was and is one of my favorite professors of all time but for whatever reason had a strange method for displaying the overheads: lay slide on overhead, turn on overhead, talk about slide, turn off overhead, lay slide on overhead, turn on overhead, etc. The overhead projector was turned on and off for every single slide. I remember joking with my classmates that any electricity savings from the two seconds it was turned off while picking up the next transparency off the pile would surely be lost by having the replace the worn out power switch before its time.
I also recall that the final paper for this class had to be cancelled. It was supposed to be a research paper, which we'd write with all our new-found communication skills. The problem was that when you do research, you have to get IRB approval, even if it's a simple survey to ask people about their parking behaviors, which ours was. IRB approval usually takes months, and while there are provisions for blanket IRB approval for class-based research projects in certain situations, it was too late for that process as well. Being the go-getters that we were (and since I was writing a paper about the same topic in my English class the same semester), we already had our research collected by the time the sad news was delivered. We ended up writing and turning in our paper anyway for extra credit. The extra credit should have been reversed, since we threw a bunch of screen beans into it, and that flies in the face of any kind of professional communication which we had been learning about. It's too bad I couldn't have taken some of that extra credit and applied it to the grade in my English class, for which I was using some of the same content.
Sunday, July 31, 2011
Three Sides to Every Story
On Friday, President Obama announced new fuel economy standards that will be worked towards over the next 15 years.
Shortly thereafter, the obviously politically neutral folks at impeachobamacampaign.com posted about how these new fuel economy standards will result in death. Simply put, cars must be lighter in order to attain higher fuel economy, but people in light cars are more likely to die when they collide with heavy vehicles.
Just two days earlier, the even more politically neutral folks at freakonomics.com posted a similar story, but unless they had some White House connections (which is possible) was unprovoked by Obama's announcement of higher standards. Their line is that heavier cars cause more deaths.
What's interesting is that the two stories are saying both the same thing yet opposite things at the same time. One claims that lighter cars result in more deaths, while the other claims that heavier cars result in more deaths. The difference is in which car they're talking about. If car A is heavier and collides with car B, car B's occupants are more likely to die. If car B is lighter and collides with car A, car B's occupants are more likely to die. What I take from this is that it's the difference between the two that counts. The relatively lighter car will always fare worse.
So if over the next couple decades, most cars get smaller, there would not likely be a large increase in deaths, because everything would get smaller at about the same rate. Some of the behemoths currently on the road will still be out there, true, but you're not going to last long driving a 20 year old SUV pulling in 12 mpg when everyone else is getting 50+ mpg.
So there's two sides to the same statistical story, but what's the third side? My side is the third side. I question whether we'll even be thinking in mpg in 15-20 years. Between CNG, electricity, Mr Fusion, methane, solar, hydrogen, etc. there are so many alternative energy sources poised to take the place of the archaic gasoline that you'd have to hope something else will have matured enough by then to take its place. At the very least, we'll see more hybrid or bi-fuel vehicles that either automatically or manually switch between gasoline and the alternative power source.
Then what if there's an alternative power source that is so cheap and powerful it allows for heavy vehicles? Add to that computers and sensors that detect imminent crashes and stop you before they happen, and it won't matter the size of your vehicle, since we won't crash into each other anyway. Perhaps cars will be driven by Google so we can just rest in our sleep pods while Google optimizes our travel for us.
Maybe 2025 is too soon to expect an accident-free, emissions-free future. After all, I'm pretty sure Back to the Future's vision of hovercars and home energy reactors won't be reality by 2015 even though they've had 30 years to work on them. But we can dream. Maybe we'll see Obama channel JFK's speech from almost 50 years ago about going to the moon and rally the country around a monumental transportation goal, ironically just after he dismantles NASA.
We'll see where the big dreams take us, but with all the fantastic possibilities my mind can dream up, 50 mpg doesn't seem like that big of a deal, even if it requires a little extra technology to keep us from crashing into each other so much.
Shortly thereafter, the obviously politically neutral folks at impeachobamacampaign.com posted about how these new fuel economy standards will result in death. Simply put, cars must be lighter in order to attain higher fuel economy, but people in light cars are more likely to die when they collide with heavy vehicles.
Just two days earlier, the even more politically neutral folks at freakonomics.com posted a similar story, but unless they had some White House connections (which is possible) was unprovoked by Obama's announcement of higher standards. Their line is that heavier cars cause more deaths.
What's interesting is that the two stories are saying both the same thing yet opposite things at the same time. One claims that lighter cars result in more deaths, while the other claims that heavier cars result in more deaths. The difference is in which car they're talking about. If car A is heavier and collides with car B, car B's occupants are more likely to die. If car B is lighter and collides with car A, car B's occupants are more likely to die. What I take from this is that it's the difference between the two that counts. The relatively lighter car will always fare worse.
So if over the next couple decades, most cars get smaller, there would not likely be a large increase in deaths, because everything would get smaller at about the same rate. Some of the behemoths currently on the road will still be out there, true, but you're not going to last long driving a 20 year old SUV pulling in 12 mpg when everyone else is getting 50+ mpg.
So there's two sides to the same statistical story, but what's the third side? My side is the third side. I question whether we'll even be thinking in mpg in 15-20 years. Between CNG, electricity, Mr Fusion, methane, solar, hydrogen, etc. there are so many alternative energy sources poised to take the place of the archaic gasoline that you'd have to hope something else will have matured enough by then to take its place. At the very least, we'll see more hybrid or bi-fuel vehicles that either automatically or manually switch between gasoline and the alternative power source.
Then what if there's an alternative power source that is so cheap and powerful it allows for heavy vehicles? Add to that computers and sensors that detect imminent crashes and stop you before they happen, and it won't matter the size of your vehicle, since we won't crash into each other anyway. Perhaps cars will be driven by Google so we can just rest in our sleep pods while Google optimizes our travel for us.
Maybe 2025 is too soon to expect an accident-free, emissions-free future. After all, I'm pretty sure Back to the Future's vision of hovercars and home energy reactors won't be reality by 2015 even though they've had 30 years to work on them. But we can dream. Maybe we'll see Obama channel JFK's speech from almost 50 years ago about going to the moon and rally the country around a monumental transportation goal, ironically just after he dismantles NASA.
We'll see where the big dreams take us, but with all the fantastic possibilities my mind can dream up, 50 mpg doesn't seem like that big of a deal, even if it requires a little extra technology to keep us from crashing into each other so much.
Wednesday, July 20, 2011
Undergraduate Research
Is anyone surprised by a study showing first year college students can't research?
They don't select good sources, don't engage with their sources, and largely copy and paste without showing real understanding of the content. Of course they don't. This really isn't a surprise, right? First year students are still unlearning high school habits. Any research they might be doing their freshman year is not going to be realistic, since they don't have deep content knowledge. Instruction in introductory writing courses is of the assembly line variety with large classes trying to meet general education requirements.
Is it a problem? I don't think so. If seniors and grad students are still writing papers like this, that's a problem. The real solution is to teach research and in-depth writing in the context of major courses. Introductory writing courses would be better off focusing on mechanics and grammar as a foundation for in-depth writing later. One step at a time.
They don't select good sources, don't engage with their sources, and largely copy and paste without showing real understanding of the content. Of course they don't. This really isn't a surprise, right? First year students are still unlearning high school habits. Any research they might be doing their freshman year is not going to be realistic, since they don't have deep content knowledge. Instruction in introductory writing courses is of the assembly line variety with large classes trying to meet general education requirements.
Is it a problem? I don't think so. If seniors and grad students are still writing papers like this, that's a problem. The real solution is to teach research and in-depth writing in the context of major courses. Introductory writing courses would be better off focusing on mechanics and grammar as a foundation for in-depth writing later. One step at a time.
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