Causal

Collaborative Research: Assisting and Assessing Middle School Science Learning in Formal and Informal Settings

Principal Investigator: 
Co-Investigator: 
Project Overview
Background & Purpose: 

We are testing the hypothesis that teaching another, in this case a Teachable Agent, improves student learning and metacognition. We are also testing the hypothesis that new technologies can improve homework compliance by making homework more like “home” and less like “school.” 

Setting: 

Studies occur in classrooms in the Bay Area and Nashville. Many studies also include work in homes, as students complete homework on-line.

Research Design: 

The research design for this project is comparative, and is designed to generate evidence that is descriptive using observation, and causal using both experimental and quasi-experimental methods. The project includes an intervention, which is that we teach children in one of two ways and compare their effects. This project collects original data using school records/ policy documents, assessments of learning/ achievement tests, videography, web logs, and, face-to-face semi-structured/ informal interviews. Data include: standard assessments of learning; automated assessments of student projects; log files of interaction data. New instruments include preparation for future learning measures; automated assessments of student projects; and Hidden Markov Model analysis of user interactions. Analysis will include General Linear Models (e.g., t-tests, regressions, MANOVA), Hidden Markov Model Analysis (a machine learning technique for finding patterns of interaction), and coding of open responses and chat logs to convert them to countable data. 

Findings: 

We have demonstrated that teaching another (computer or person) leads to (a) better conceptual learning, (b) more productive motivations for learning, (c) improved levels of metacognition.

We have demonstrated that using an on-line game that involves teaching an agent improves homework compliance and preparation for learning in school after the homework. 

Publications & Presentations: 

Chase, C., Chin, D. B., Oppezzo, M., & Schwartz, D. L. (in press). Teachable agents and the protégé effect: Increasing the effort towards learning. Journal of Science Education and Technology.

Lindgren, R., & Schwartz, D. L. (2009). Spatial learning and computer simulations in science. International Journal of Science Education, 31(3), 419-438.

Blair, K., Schwartz, D. L., Biswas, G., & Leelawong, K. (2007). Pedagogical agents for learning by teaching: Teachable Agents. Educational Technology, (47)1, 56-61.

Okita, Y. S., & Schwartz, D. L. (2006) Young Children’s Understanding of Animacy and Entertainment Robots, International Journal of Humanoid Robotics, 3, 393-412.

Schwartz, D. L., Blair, K. P., Biswas, G., Leelawong, K., & Davis, J. (2007). Animations of thought: Interactivity in the teachable agents paradigm. In R. Lowe & W. Schnotz (Eds). Learning with Animation: Research and Implications for Design (pp. 114-40). UK: Cambridge University Press.

Schwartz, D. L., Chase, C., Wagster, J., Okita, S., Roscoe, R., Chin, D., & Biswas, G. (in press). Interactive metacognition: Monitoring and regulating a teachable agent. To appear in D. J. Hacker, J. Dunlosky, and A. C. Graesser (Eds.), Handbook of Metacognition in Education.

Hogyeong, J., Gupta, A., Roscoe, R., Wagster, J. Biswas, G., & Schwartz, D. (2008). Using hidden Markov models to characterize student behaviors in learning-by-teaching environments. Lecture Notes in Computer Science: Intelligent Tutoring Systems (p. 614-625). Berlin, Springer.

Okita, S.Y., Bailenson, J., Schwartz, D. L. (2007). The mere belief of social interaction improves learning. In D. S. McNamara & J. G. Trafton (Eds.), The Proceedings of the 29th Meeting of the Cognitive Science Society (pp. 1355-1360). August, Nashville, USA.

Wagster, J., Tan, J., Wu, Y., Biswas, G., & Schwartz, D. L. (2007). Do learning by teaching environments with metacognitive support help students develop better learning behaviors? The Proceedings of the 29th Meeting of the Cognitive Science Society (pp. xxx-xxx). August, Nashville, USA.

Hogyeong, J., Gupta, A., Roscoe, R., Wagster, J. Biswas, G., & Schwartz, D. (2008). Using hidden Markov models to characterize student behaviors in learning-by-teaching environments. Lecture Notes in Computer Science: Intelligent Tutoring Systems (p. 614-625). Berlin, Springer.

Other Products: 

We are developing a learning technology that includes teacher tools for creating, adopting, and sharing curricula using the teachable agents. 

State: 
Research Design: 

Pages

Subscribe to RSS - Causal