Stoichiometry Study

Studying the Learning Effect of Personalization and Worked Examples in the Solving of Stoichiometry Problems

Bruce McLaren, Ken Koedinger, and Dave Yaron

Abstract

In this project, conducted within the PSLC Chemistry LearnLab, we have been investigating whether personalized instructional materials and worked examples can improve learning when used as complementary techniques together with an intelligent tutoring system. The study involves online (i.e., web-based) learning of stoichiometry, the basic math required to solve many chemistry problems, and uses intelligent tutoring systems developed with the aid of the Cognitive Tutor Authoring Tools (CTAT), a key enabling technology of the PSLC. The stoichiometry materials were piloted at CMU and the University of Pittsburgh and have now been used in three separate in vivo studies with students at a university and three high schools.

In a recent book by Clark and Mayer (2003), a number of principles were proposed as guidelines for building e-Learning systems. All are supported by multiple educational psychology and cognitive science studies. We were especially interested in and decided to experiment with two of the Clark and Mayer principles:

• Personalization Principle One: Use Conversational Rather than Formal Style
• Worked Example Principle One: Replace Some Practice Problems with Worked Examples

In contrast with most previous studies, however, we wished to test these principles in the context of a web-based intelligent tutoring system (ITS), rather than on their own in a standard e-Learning or ITS environment or, as in even earlier studies, in conjunction with problems solved by hand. The key difference is that an intelligent tutoring system provides more than just problem solving practice; it also supplies students with context-specific hints and feedback on their progress. In particular, we were interested in whether personalized, polite language within an ITS and worked examples provided as complements to ITS-supported problems might improve learning beyond the gains from the ITS on its own. This study was first piloted at CMU and the University of Pittsburgh and executed in full with students enrolled in an Intro to Chemistry class at the University of British Columbia (UBC). The results of the initial study are reported in (McLaren et al, 2006). A second study was done with high school chemistry students at Hampton High School in Pittsburgh and with chemistry students at a high school in northern New Jersey. A third study, in which positive and negative “face” are tested (Mayer, Johnson, Shaw, and Sandhu, 2006; Brown and Levinson, 1987), in addition to personalized language, is currently underway and will be completed in the late winter of 2006/2007.

Glossary

• E-Learning Principles: Guidelines for delivering online materials (text, picture, videos, tutors) that provide learning benefits and which have been tested in scientific studies.
• Personalization: Presenting the student with first and second person feedback and hints, as well as polite and encouraging language
• Worked Examples: Step-by-step solutions to problems, together with explanations of each step, presented in textual, graphical, or video format

Research Question

Can personalized, polite hints, feedback, and messages lead to robust learning when used in conjunction with a highly supportive learning environment, in particular an intelligent tutoring system?

Can worked examples lead to robust learning when used in conjunction with a highly supportive learning environment, in particular an intelligent tutoring system?

Background and Significance

The Clark and Mayer personalization principle proposes that informal speech or text (i.e., conversational style) is more supportive of learning than formal speech or text in an e-Learning environment. In other words, instructions, hints, and feedback should employ first or second-person language (e.g., “You might want to try this”) and should be presented informally (e.g., “Hello there, welcome to the Stoichiometry Tutor! …”) rather than in a more formal tone (e.g., “Problems such as these are solved in the following manner”).

Although the personalization principle runs counter to the intuition that information should be “efficiently delivered” and provided in a business-like manner to a learner, it is consistent with cognitive theories of learning. For instance, educational research has demonstrated that people put forth a greater effort to understand information when they feel they are in a dialogue (Beck, McKeown, Sandora, Kucan, and Worthy, 1996). While consumers of e-Learning content certainly know they are interacting with a computer, and not a human, personalized language helps to create a “dialogue” effect with the computer. E-Learning research in support of the personalization principle is somewhat limited but at least one project has shown positive effects (Moreno and Mayer, 2000). Students who learned from personalized text in a botany e-Learning system performed better on subsequent transfer tasks than students who learned from more formal text in five out of five studies. Note that this project did not explore the use of personalization in a web-based intelligent tutoring setting, as we are doing in our work.

The Clark and Mayer worked example principle proposes that an e-Learning course should present learners with some step-by-step solutions to problems (i.e., worked examples) rather than having them try to solve all problems on their own. Interestingly, this principle also runs counter to many people’s intuition and even to research that stresses the importance of “learning by doing” (Kolb, 1984).

The theory behind worked examples is that solving problems can overload limited working memory, while studying worked examples does not and, in fact, can help build new knowledge (Sweller, 1994). The empirical evidence in support of worked examples is more established and long standing than that of personalization. For instance, in a study of geometry by Paas (1992), students who studied 8 worked examples and solved 4 problems worked for less time and scored higher on a posttest than students who solved all 12 problems. In a study in the domain of probability calculation, Renkl (1997) found that students who employed more principle-based self-explanations benefited more from worked examples than those who did not. Research has also shown that mixing worked examples and problem solving is beneficial to learning. In a study on LISP programming (Trafton and Reiser, 1993), it was shown that alternating between worked examples and problem solving was more beneficial to learners than observing a group of worked examples followed by solving a group of problems.

Previous ITS research has investigated how worked examples can be used to help students as they problem solve (Gott, Lesgold, and Kane, 1996; Aleven and Ashley, 1997). Conati’s and VanLehn’s SE-Coach demonstrated that an ITS can help students self-explain worked examples (2000). However, none of this prior work explicitly studied how worked examples, presented separately from supported problem solving as complementary learning devices, might provide added value to learning with an ITS and avoid cognitive load (Sweller, 1994). Closest to our approach is that of Mathan and Koedinger (2002). They experimented with two different versions of an Excel ITS, one that employed an expert model and one that used an intelligent novice model, complemented by two different types of worked examples, “active” example walkthroughs (examples in which students complete some of the work) and “passive” examples (examples that are just watched). The “active” example walkthroughs led to better learning but only for the students who used the expert model ITS. However, a follow-up study did not replicate these results (Mathan, 2003). This work, as with the other ITS research mentioned above, was not done in the context of a web-based ITS.

Independent Variables

To test our hypotheses and the effect of personalization and worked examples on (e)learning, we designed and executed a 2 x 2 factorial study.

• 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 supported problem solving only and the other supported problem solving and 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 problem. This alternating technique has yielded better learning results in prior research (Trafton and Reiser, 1993).

Dependent Variables

Students are asked to solve pre and post-test stoichiometry problems which are isomorphic to one another and to the tutored problems that are part of the study intervention. Thus, the initial version of the study has focused exclusively on near transfer learning.

While we eventually plan to test other aspects of robust learning, in particular, far transfer and accelerated future learning, we believe it does not make sense to do so until (and if) we achieve results with near transfer. Thus far our results, as reported in McLaren et al, 2006, have shown that neither personalization nor worked examples make a difference to near transfer learning when these interventions are used as complements to an intelligent tutoring system.

Hypothesis

The project tests two hypotheses:

H1

The use of polite, conversational problem statements, feedback, and hints in a supported problem-solving environment (i.e., an intelligent tutoring system) can improve learning in an e-Learning system.

H2

The use of worked examples in a supported problem-solving environment (i.e., an intelligent tutoring system) can improve learning in an e-Learning system.

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, polite language used together lead to more robust learning and whether an ITS and worked examples used together lead to more robust learning.

Annotated Bibliography

• Aleven, V. & Ashley, K. D. (1997). Teaching Case-Based Argumentation Through a Model and Examples: Empirical Evaluation of an Intelligent Learning Environment, Proceedings of AIED-97, 87-94.
• Beck, I., McKeown, M. G., Sandora, C., Kucan, L., and Worthy, J. (1996). Questioning the author: A year long classroom implementation to engage students in text. Elementary School Journal, 96, 385-414.
• 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.
• Mathan, S. and Koedinger, K. R. (2002). An Empirical Assessment of Comprehension Fostering Features in an Intelligent Tutoring System. Proceedings of ITS-2002. Lecture Notes in Computer Science, Vol. 2363, 330-343. Berlin: Springer-Verlag
• 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.
• 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), Jhongli, Taiwan, June 26-30, 2006.
• McLaren, B. M. Presentation to the NSF Site Visitors, June, 2006.
• 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.