Baker Choices in LE Space

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How Content 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, 2005; Baker, in press a; Rodrigo et al, in press). However, other recent research has found that differences between lessons are on the whole better predictors of gaming than differences between students (Baker, in press b), 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

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 hint abuse (Aleven & Koedinger, 2001), systematic guessing (Baker et al, 2004), and the failure to self-explain.

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). 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

To test our hypotheses and the effect of personalization and worked examples on learning, we designed and have executed two 2 x 2 factorial studies.

  • One independent variable is Personalization, with one level impersonal instruction, feedback, and hints and the other personal instruction, feedback, and hints.
  • The other independent variable is Worked Examples, with one level tutored problem solving alone and the other tutored problem solving together with worked examples. In the former condition, subjects only solve problems using the intelligent tutor; no worked examples are presented. In the latter condition, subjects alternate between observation and self-explanation of a worked example and solving of a tutored problem. This alternating technique has yielded better learning results in prior research (Trafton and Reiser, 1993).

With respect to personalized language (and because we got a null result in the first two studies), we thought that perhaps our conceptualization and implementation might not be as socially engaging as we had hoped. This was also suggested to us by Rich Mayer, who reviewed the first study. In a recent study that Mayer and colleagues did (Wang, Johnson, Mayer, Rizzo, Shaw, & Collins, in press), based on the work of Brown and Levinson (1987), they found that a polite version of a tutor, which provided polite feedback such as, “You could press the ENTER key”, led to significantly better learning than a direct version of the tutor that used more imperative feedback such as, “Press the ENTER key.” We decided to investigate this in a third in vivo study in which we changed all of the personalized instruction, feedback, and hints of the tutor to more polite forms, similar to that used by Mayer and colleagues.

Thus, for the third study we changed the first independent variable to "Politness," with one level polite instruction, feedback, and hints and the other direct instruction, feedback, and hints. The 2 x 2 factorial design for our third and most recent study is shown below.

Fig2-FactorialDesign.jpg

Below is a table that provides examples of the differences in language between the polite version of our tutor and earlier versions.

Table1-FeedbackDiffs.jpg

Dependent Variables

To evaluate learning, students are asked to solve pre and post-test stoichiometry problems that are isomorphic to one another and to the tutored problems. Thus, we have focused on normal post-tests in our studies so far. When (and if) we see an effect in a normal post-test, we will conduct studies to test retention.

Findings

In two initial 2 x 2 factorial studies, we found that personalized language and worked examples had no significant effects on learning, thus not supporting hypotheses H1 and H2. On the other hand, there was a significant difference between the pre and posttest in all conditions, suggesting that the intelligent tutor present in all conditions did make a difference in learning. For study 1 we had N = 63 and for study 2 we had N = 76. The results of Study 1 are reported in (McLaren, Lim, Gagnon, Yaron, and Koedinger, 2006). We are currently analyzing the data to test hypothesis H3, that is, to determine if learning with worked examples was more efficient.

One possible explanation for why neither personalized language nor worked examples have made a difference thus far is the switch from a lab environment to in vivo experimentation. Most of the results from past studies of both personalized language and worked examples come from lab studies, so it may simply be that the realism and messiness of an in vivo study makes it much more difficult for interventions such as these to make a difference to students’ learning. It may also be that the tutoring received by the subjects simply had much more effect on learning than the worked examples or personalized language.

We recently concluded the third study in which we investigated the use of polite language, rather than personalized language (as shown in the table above). We have so far only analyzed the first 33 subjects, out of N=84 (for details on the analysis of the first 33 subjects see McLaren, Lim, Yaron, & Yaron, in press). The preliminary data indicates that the polite condition leads to larger learning gains than the non-polite condition, however, not at a statistically significant level. Worked examples also did not make a difference to learning. Thus, once again, hypotheses H1 and H2 were not supported. We are in the process of analyzing the rest of the data from the remaining subjects who participated in this study, including an investigation of hypothesis H3 and the efficiency of learning.

Explanation

This study is part of the Coordinative Learning cluster. The study follows the Coordinative Learning hypothesis that two (or more) sources of instructional information can lead to improved robust learning. In particular, the study tests whether an ITS and personalized (or polite) language used together lead to more robust learning and whether an ITS and worked examples used together lead to more robust learning.

Connections to Other PSLC Studies

  • Our project also relates to the Aleven and Butcher project in that we both are exploring the learning value of e-Learning Principles (they are investigating Contiguity; we are looking at personalization and worked examples). In addition, like their study, we are prompting self-explanation as a means to promote robust learning.

Annotated Bibliography

  • McLaren, B. M., Lim, S., Yaron, D., and Koedinger, K. R. (2007). Can a Polite Intelligent Tutoring System Lead to Improved Learning Outside of the Lab? In the Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED-07), pp 331-338. [pdf file]
  • McLaren, B. M., Lim, S., Gagnon, F., Yaron, D., and Koedinger, K. R. (2006). Studying the Effects of Personalized Language and Worked Examples in the Context of a Web-Based Intelligent Tutor; In the Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS-2006), pp. 318-328. [pdf file]
  • McLaren, B. M. Presentation to the NSF Site Visitors, June, 2006.

References

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  • Brown, P. and Levinson, S. C. (1987). Politeness: Some Universals in Language Use. Cambridge University Press, New York.
  • Clark, R. C. and Mayer, R. E. (2003). e-Learning and the Science of Instruction. Jossey-Bass/Pfeiffer.
  • Conati, C. and VanLehn, K. (2000). Toward Computer-Based Support of Meta-Cognitive Skills: a Computational Framework to Coach Self-Explanation. Int’l Journal of Artificial Intelligence in Education, 11, 398-415.
  • Gott, S. P., Lesgold, A., & Kane, R. S. (1996). Tutoring for Transfer of Technical Competence. In B. G. Wilson (Ed.), Constructivist Learning Environments, 33-48, Englewood Cliffs, NJ: Educational Technology Publications.
  • Kolb, D. A. (1984). Experiential Learning - Experience as the Source of Learning and Development, Prentice-Hall, New Jersey. 1984.
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  • Mathan, S. (2003). Recasting the Feedback Debate: Benefits of Tutoring Error Detection and Correction Skills. Ph.D. Dissertation, Carnegie Mellon Univ., Pitts., PA.
  • Mayer, R. E., Johnson, W. L., Shaw, E. and Sandhu, S. (2006). Constructing Computer-Based Tutors that are Socially Sensitive: Politeness in Educational Software, International Journal of Human-Computer Studies 64 (2006) 36-42.
  • Moreno, R. and Mayer, R. E. (2000). Engaging students in active learning: The case for personalized multimedia messages. Journal of Ed. Psych., 93, 724-733.
  • Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive load approach. Journal of Ed. Psych., 84, 429-434.
  • Renkl, A. (1997). Learning from Worked-Out Examples: A Study on Individual Differences. Cognitive Science, 21, 1-29.
  • Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295-312.
  • Trafton, J. G. and Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In M. Polson (Ed.) Proceedings of the 15th annual conference of the Cognitive Science Society, 1017-1022.
  • Wang, N., Johnson, W. L., Mayer, R. E., Rizzo, P., Shaw, E., & Collins, H. (in press). The Politeness Effect: Pedagogical Agents and Learning Outcomes. To be published in the International Journal of Human-Computer Studies.
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