Walker A Peer Tutoring Addition
Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition
Erin Walker, Bruce M. McLaren, Nikol Rummel, and Ken Koedinger
Summary Tables
| PIs | Bruce McLaren, Nikol Rummel |
| Other Contributers |
|
Pre Study 1
| Study Start Date | 05/06 |
| Study End Date | 06/06 |
| LearnLab Site | CWCTC |
| LearnLab Course | Algebra |
| Number of Students | N = 14 |
| Average # of hours per participant | 3 hrs. |
Pre Study 2
| Study Start Date | 11/06 |
| Study End Date | 11/06 |
| LearnLab Site | CWCTC |
| LearnLab Course | Algebra |
| Number of Students | N = 20 |
| Average # of hours per participant | 3 hrs. |
Full Study
| Study Start Date | 04/07 |
| Study End Date | 04/07 |
| LearnLab Site | CWCTC |
| LearnLab Course | Algebra |
| Number of Students | N = 70 |
| Average # of hours per participant | 3 hrs. |
Abstract
We wish to evaluate the potential benefits of combining peer tutoring with a cognitive tutor. The study compares an experimental condition to control conditions. In the experimental condition, students first prepare to tutor, and then take turns tutoring each other using the cognitive tutor interface. They receive help from the cognitive tutor as they collaborate. In the first control, students prepare to tutor and tutor each other, but receive no help from the cognitive tutor. In the second control, students solve problems alone with the help of the cognitive tutor.
In this in vivo experiment in the Algebra LearnLab, three classes will be used with roughly 20 students per class. The study will last a week, and students will work on problems from an equation solving unit. Robust learning will be measured.
Background and Significance
Which approach is a better way of increasing student knowledge: intelligent tutoring or collaborative learning? On the one hand, the structured problem-solving and individualized feedback provided by intelligent tutoring systems (ITS) have been very successful at improving student learning in real classrooms. For example, the Cognitive Tutor Algebra-1 (CTA) has been shown to improve algebra understanding by about one standard deviation over traditional classroom instruction (Koedinger, Anderson, Hadley, & Mark, 1997). However, critics of the intelligent tutoring system approach might argue that students acquire mainly shallow knowledge while using these tutors, because they are not constructing their own knowledge. Intelligent tutoring systems are currently limited in their interaction with students, and students are limited in the problem-solving paths that they can take while using the system. Collaborative learning, on the other hand, is a learning arrangement that allows students to interact more deeply and freely. Collaboration has been shown to have positive effects on student domain knowledge and reasoning strategies, but is only effective when students interact in beneficial ways, such as by providing each other with useful help and feedback (Johnson and Johnson, 1990) and jointly elaborating on the learning content (Teasley, 1995). Unfortunately, in a real-world context students often do not use such positive collaborative behaviors spontaneously and do not receive sufficient guidance from their teacher. For example, schools that use the CTA also adopt a curriculum that emphasizes collaborative problem-solving, but these collaborative activities are often not correctly administered by teachers or followed by students (Ritter, Blessing, & Hadley, 2002). To encourage the types of positive collaborative behaviors that lead students to deeply process the material, researchers structure collaborative interactions by developing scripts that designate roles and activities for participating students (for an overview, see Kollar, Fischer, and Hesse, in press). In sum, the advantages and disadvantages of the intelligent tutoring and collaborative learning are complimentary: The guided problem-solving provided by an ITS is effective but limits student interaction with knowledge in the domain, while collaborative activities increase the potential for the acquisition of deep knowledge but do not always provide sufficient guidance for students to tap this potential.
The best approach might be to combine collaborative activities with intelligent tutoring. Collaborative interaction could augment the effects of an existing ITS by adding deeper interaction, and intelligent tutoring support could improve the quality of student collaboration by providing guidance to students as they attempt to follow a collaboration script. One way to incorporate collaboration into an ITS is to place the intelligent tutoring system in the role of one of the collaborators and have a student interact with this agent. For example, Biswas, Schwartz, Leelawong, Vye, and the TAG-V (2005) have the student collaborate with an agent (“Betty”) in a peer tutoring scenario. The student teaches Betty about ecosystems with the help of a second mentoring agent (“Mr. Davis”), and it has been shown that a student who takes the role of a teacher in this system benefits more than a student who takes the role of a learner in a parallel traditional tutoring system. However, this system does not yet support natural language interaction between the agents involved, and therefore does not take full advantage of important collaborative interaction mechanisms that can increase learning. Another way to combine collaboration and tutoring is to use the ITS to structure and tutor the student collaboration, by connecting collaborative tools with ITS components (see Harrer, McLaren, Walker, Bollen, & Sewall, 2006), and developing a model of good and ineffective collaboration in order to provide on-line hints and feedback (see Soller, 2001). Most research into computer tutoring of the collaboration between two students is still in a preliminary stage.
We integrate collaborative learning with an ITS using a peer tutoring framework. Similar to Biswas et al. (2005), we put the student in the role of the tutor, and intend to have the cognitive elements of the tutoring supported by another agent. However, unlike Biswas et al., (2005), the teachable agent is a real student. Therefore, we have intelligent tutoring components monitoring and providing feedback on student collaboration in addition to student cognition, like Harrer et al. (2006). Our ultimate goal is to allow students to tutor each other through the interface of an ITS, supported by both cognitive and collaborative tutoring.
Glossary
- Cognitive tutor – the computer tutor agent
- Peer tutor – the human tutor agent
- Student – the person being tutored
Research question
Do instructional activities that involve three agents (a student, a peer tutor, and a computer tutor) increase robust learning compared to instructional activities that involve two agents (student and peer tutor, student and computer tutor)?
Independent variables
We vary the agents involved in the interaction. The condition where a student interacts with a peer tutor and computer tutor is considered the “peer + cognitive tutoring” condition. The condition where a student interacts only with a peer tutor is considered the “peer tutoring” condition. The condition where a student interacts only with a computer tutor is considered the “cognitive tutoring” condition.
Hypothesis
When students attempt to solve a problem with the help of both a peer tutor and cognitive tutor, they should show more robust learning than when they problem solve with the help of only one of the tutors. A student/cognitive tutor collaboration should produce similar levels of robust learning as a student/peer tutor collaboration, as long as the peer tutor is sufficiently prepared.
Dependent variables
- Near transfer, immediate: During training, student progress on training problems will be analyzed.
- Near transfer, retention: Students will be given a posttest immediately after the study on similar problems
- Acceleration of future learning: Student learning on future equation solving units will be measured.
Explanation
This study is part of the Interactive Communication cluster, and its hypothesis is a specialization of the IC cluster’s central hypothesis. We assume that the instruction is in or above the student’s ZPD. Here is what we expect to happen in the three conditions:
- In the peer tutoring condition, the peer tutor’s help should increase learning compared to a student learning with the cognitive tutor. However, the student may make too many errors and/or require too much communication with the second agent. The peer tutor may not be able to provide the student with the help he/she needs. Learning will not be optimized.
- In the cognitive+peer tutoring condition, the peer tutor’s help should provide the additional benefits of interactive communication, and the cognitive tutor’s help should ensure that both the student and the peer tutor get the problem solving assistance they need. The support of the cognitive tutor and interaction with the student will also increase the peer tutor’s learning.
The target path will have the students learning-by-doing and learning-by-teaching, with the help of the computer agent so students do not make too many errors or require too much communication.
Findings
A previous study involving only peer tutoring suggested that cognitive tutoring is indeed necessary so that problems are in the ZPD of the student and peer tutor. The current study will be completed in the fall.
Annotated bibliography
Walker, E., Rummel, N., McLaren, B. M. & Koedinger, K. R. (submitted). The Student Becomes the Master: Integrating Peer Tutoring with Cognitive Tutoring. Submitted to the conference on Computer Supported Collaborative Learning (CSCL-07). Rutgers University, July 16-21, 2007.
Walker, E., Koedinger, K., McLaren, B. M., & Rummel, N. (2006). Cognitive tutors as research platforms: Extending an established tutoring system for collaborative and metacognitive experimentation. Lecture Notes in Computer Science, Volume 4053/2006. Proceedings of the 8th International Conference on Intelligent Tutoring Systems (pp. 207-216). Berlin: Springer
Walker, E. (2005). Mutual peer tutoring: A collaborative addition to the Algebra-1 Cognitive Tutor. Paper presented at the 12th International Conference on Artificial Intelligence and Education (AIED-05, Young Researchers Track), July, 2005, Amsterdam, the Netherlands.
References
Biswas, G., Schwartz, D. L., Leelawong, K., Vye, N., & TAG-V. (2005). Learning by teaching: A new agent paradigm for educational software. Applied Artificial Intelligence, 19, 363–392.
Carmien, S., Kollar, I., Fischer, G. & Fischer, F. (2006). The interplay of internal and external scripts. In F. Fischer, I. Kollar, H. Mandl &, J. Haake, Scripting computer-supported communication of knowledge. Cognitive, computational, and educational perspectives (pp. 289-311). New York: Springer.
Fantuzzo, J. W., Riggio, R. E., Connelly, S., & Dimeff, L. A. (1992). Effects of reciprocal peer tutoring on mathematics and school adjustment: A componential analysis. Journal of Educational Psychology, 84(3), 331-339.
Fuchs, L.S., Fuchs, D., Prentice, K., Burch, M., Hamlett, C.L., Owen, R., & Schroeter, K. (2003). Enhancing third-grade students' mathematical problem solving with self-regulated learning strategies. Journal of Educational Psychology, 95(2), 306-315.
Harrer, A., McLaren, B. M., Walker, E., Bollen, L., and Sewall, J. (2006). Creating Cognitive Tutors for Collaborative Learning: Steps Toward Realization. User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI) (2006) 16: 175-209.
Johnson, D. W. and Johnson, R. T. (1990). Cooperative learning and achievement. In S. Sharan (Ed.), Cooperative learning: Theory and research (pp. 23-37). New York: Praeger.
King, A., Staffieri, A., & Adelgais, A. (1998). Mutual peer tutoring: Effects of structuring tutorial interaction to scaffold peer learning. Journal of Educational Psychology, 90, 134-152.
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30-43.
Kollar, I., Fischer, F., & Hesse, F. W. (in press). Collaboration scripts - a conceptual analysis. Educational Psychology Review.
Ploetzner, R., Dillenbourg, P., Preier, M.,&Traum, D. (1999). Learning by explaining to oneself and to others. In P. Dillenbourg (Ed.), Collaborative learning. Cognitive and computational approaches (pp. 103–121). Amsterdam: Pergamon.
Ritter, S., Blessing, S. B., & Hadley, W. S. (2002). SBIR Phase I Final Report 2002. Department of Education. Department of Education RFP ED: 84-305S.
Rummel, N., & Spada, H. (2005). Learning to collaborate: An instructional approach to promoting collaborative problem-solving in computer-mediated settings. Journal of the Learning Sciences, 14(2), 201--241.
Soller, A.L. (2001). Supporting Social Interaction in an Intelligent Collaborative Learning System. International Journal of Artificial Intelligence in Education, 12, 40-62.
Teasley, S. D. (1995). The role of talk in children's peer collaborations. Developmental Psychology, 31(2), 207-220.
Walker, E., Koedinger, K. R., McLaren, B. M. and Rummel, N. Cognitive Tutors as Research Platforms: Extending an Established Tutoring System for Collaborative and Metacognitive Experimentation. In the Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, June 26-30, 2006.
Webb, N.M., Troper, J.D., & Fall, R. (1995). Constructive activity and learning in collaborative small groups. Journal of Educational Psychology, 87, 406-423.
