Baker Choices in LE Space

== How Contenthttp://www.learnlab.org/research/wiki/skins/common/images/button_media.png Media file link and Interface Features Influence Student Choices Within the Learning Spaces==

Ryan S.J.d. Baker, Albert T. Corbett, Kenneth R. Koedinger, Ma. Mercedes T. Rodrigo

Overview

PIs: Ryan S.J.d Baker

Co-PIs: Albert T. Corbett, Kenneth R. Koedinger

Others who have contributed 160 hours or more:

• Jay Raspat, Carnegie Mellon University, taxonomy development
• Adriana M.J.A. de Carvalho, Carnegie Mellon University, data coding

Others significant personnel :

• Ma. Mercedes T. Rodrigo, Ateneo de Manila University, data coding methods
• Vincent Aleven, Carnegie Mellon University, taxonomy development

Abstract

We are investigating what factors lead students to make specific path choices in the learning space, focusing specifically on the shallow strategy known as gaming the system. Prior research has shown that a variety of motivations, attitudes, and affective states are associated with the choice to game the system (Baker et al, 2004; Baker, in press b; Rodrigo et al, 2007). However, other recent research has found that differences between lessons are on the whole better predictors of gaming than differences between students (Baker, 2007), suggesting that contextual factors associated with a specific tutor unit may be the most important reason why students game the system. Hence, this project is investigating how the content and presentational/interface aspects of a learning environment influence whether students tend to choose a gaming the system strategy.

To this end, we are in the process of annotating each learning event/transaction in a set of units in the Algebra LearnLab and the Middle School Cognitive Tutor with descriptions of its content and interface features, using a combination of human coding and machine learning. We are also in the process of adapting a detector of the shallow gaming strategy developed in a subset of the Middle School Cognitive Tutor to the LearnLabs, in order to use the gaming detector to predict exactly which learning events in the data involve shallow gaming strategies. After completing these steps, we will use data mining to connect gaming path choices with the content and interface features of the learning events they occur in. This will give us insight into why students make specific path choices in the learning space, and explain the prior finding that path choices differ considerably between tutor units.

Glossary

• Game the system
• Help abuse
• Systematic Guessing

Research Questions

What factors lead to the choice to game the system?

What factors make detectors of gaming behavior more or less likely to transfer successfully between tutor units?

Hypothesis

H1

Content or interface features better explain differences in gaming frequency than stable between-student differences

Background and Significance

In recent years, there has been considerable interest in how students choose to interact with learning environments. At any given learning event, a student may choose from a variety of learning-oriented "deep" paths, including attempting to construct knowledge to solve a problem on one’s own (Brown and vanLehn, 1980), self-explaining (Chi et al, 1989; Siegler, 2002), and seeking help and thinking about it carefully (Aleven et al, 2003). Alternatively, the student may choose from a variety of non-learning oriented "shallow" strategies, such as Help Abuse (Aleven & Koedinger, 2001), Systematic Guessing (Baker et al, 2004), and the failure to engage in Self-explanation.

One analytical tool with considerable power to help learning scientists explain the ways students choose to use a learning environment is the learning event space. In a learning event space, the different paths a student could take are enumerated, and the effects of each path are detailed, both in terms of how the path influences the student’s success within the environment, and the student’s learning. The learning event space model provides a simple way to identify the possible paths and effects; it also provides a concrete way to break down complex research questions into simpler and more concrete questions.

Gaming the system is an active and strategic type of shallow strategy known to occur in many types of learning environments (cf. Baker et al, 2004; Cheng and Vassileva, 2005; Rodrigo et al, 2007), including the Cognitive Tutors used in LearnLab courses (Baker et al, 2004). It was earlier hypothesized that gaming stemmed from stable differences in student goals, motivation, and attitudes -- however multiple studies have now suggested that these constructs play only a small role in predicting gaming behavior (Baker et al, 2005; Walonoski & Heffernan, 2006; Baker et al, in press b). By contrast, variation in short-term affective states and the tutor lesson itself appear to play a much larger role in the choice to game (Rodrigo et al, 2007; Baker, 2007).

In this project, we investigate what it is about some tutor lessons that encourages or discourages gaming. This project will help explain why students choose shallow gaming strategies at some learning events and not at others. This will contribute to our understanding of learning event spaces, and will make a significant contribution to the PSLC Theoretical Framework, by providing an account for why students choose the shallow learning strategies in many of the learning event space models in the PSLC Theoretical Framework. It will also jump-start the process of studying why students choose other shallow learning strategies beyond gaming the system, by providing a methodological template that can be directly applied in future research, as well as initial hypotheses to investigate.

Independent Variables

We are in the process of developing a taxonomy for how intelligent tutor units can differ from one another. We have developed the list of differences that will go into the taxonomy, but have not yet fully organized them into a taxonomy. We are now labeling every unit in the Algebra LearnLab and the Middle School Cognitive Tutor with these taxonomic features. Then these features will make up the independent variables in this project.

Dependent Variables

We are in the process of labeling around 1.2 million transactions in data from the DataShop with predictions as to whether it is an instance of gaming the system. We have completed this process with the Algebra tutor. These predictions are being created by using text replay observations (Baker, Corbett, & Wagner, 2006) to label a representative set of transactions, and then using these labels to create gaming detectors (cf. Baker, Corbett, & Koedinger, 2004; Baker et al, in press a) which can be used to label the remaining transactions.

Findings

Once all lessons are labeled according to the taxonomy features, and all transactions are labeled as gaming or not gaming, we will determine which features of a lesson predict whether students will game more or less. This will occur in Spring 2008.

A preliminary finding is that the difference between lessons is a significantly better predictor than the difference between students in determining how much gaming behavior a student will engage in, in a given lesson. Put more simply, knowing which lesson a student is using is a better predictor of how much gaming will occur, than knowing which student it is.

In the Middle School Tutor, lesson has 35 parameters and achieves an r-squared of 0.55. Student has 240 parameters and achieves an r-squared of 0.16. In the Algebra Tutor, lesson has 21 parameters and achieves an r-squared of 0.18. Student achieves an equal r-squared, but with 58 students; hence, lesson is a statistically better predictor because it achieves equal or significantly better fit with considerably fewer parameters.

File:Http://www.joazeirodebaker.net/algebra-lessons-gaming.jpg

References

Aleven, V., Koedinger, K.R. (2001) Investigations into Help Seeking and Learning with a Cognitive Tutor. In R. Luckin (Ed.), Papers of the AIED-2001 Workshop on Help Provision and Help Seeking in Interactive Learning Environments (2001) 47-58

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), 277-320.

Baker, R.S.J.d. (2007) Is Gaming the System State-or-Trait? Educational Data Mining Through the Multi-Contextual Application of a Validated Behavioral Model. Complete On-Line Proceedings of the Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling 2007, 76-80. pdf

Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z. (2004) Off-Task Behavior in the Cognitive Tutor Classroom: When Students “Game the System”. Proceedings of ACM CHI 2004: Computer-Human Interaction, 383-390.pdf

Baker, R.S.J.d., Corbett, A.T., Roll, I., Koedinger, K.R. (in press a) Developing a Generalizable Detector of When Students Game the System To appear in User Modeling and User-Adapted Interaction. pdf

Baker, R.S., Corbett, A.T., Wagner, A.Z. (2006) Human Classification of Low-Fidelity Replays of Student Actions. Proceedings of the Educational Data Mining Workshop at the 8th International Conference on Intelligent Tutoring Systems, 29-36. pdf

Baker, R.S., Roll, I., Corbett, A.T., Koedinger, K.R. (2005) Do Performance Goals Lead Students to Game the System. Proceedings of the 12th International Conference on Artificial Intelligence in Education, 57-64. pdf

Baker, R.S.J.d., Walonoski, J.A., Heffernan, N.T., Roll, I., Corbett, A.T., Koedinger, K.R. (in press b) Why Students Engage in "Gaming the System" Behavior in Interactive Learning Environments. To appear in Journal of Interactive Learning Research. pdf

Brown, J.S., vanLehn, K. (1980) Repair theory: A generative theory of bugs in procedural skills. Cognitive Science, 4, 379-426.

Cheng, R., Vassileva, J. (2005) Adaptive Reward Mechanism for Sustainable Online Learning Community. Proceedings of the 12th International Conference on Artificial Intelligence in Education, 152-159.

Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P., Glaser, R. (1989) Self-Explanations: How Students Study and Use Examples in Learning to Solve Problems. Cognitive Science, 13, 145-182.

Rodrigo, M.M.T., Baker, R.S.J.d., Lagud, M.C.V., Lim, S.A.L., Macapanpan, A.F., Pascua, S.A.M.S., Santillano, J.Q., Sevilla, L.R.S., Sugay, J.O., Tep, S., Viehland, N.J.B. (2007) Affect and Usage Choices in Simulation Problem Solving Environments. Proceedings of Artificial Intelligence in Education 2007, 145-152. pdf

Siegler, R.S. (2002) Microgenetic Studies of Self-Explanations. In N. Granott & J. Parziale (Eds.), Microdevelopment: Transition processes in development and learning, 31-58. New York: Cambridge University.

Walonoski, J.A., Heffernan, N.T. (2006) Detection and Analysis of Off-Task Gaming Behavior in Intelligent Tutoring Systems. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 382-391.