|Title:||A Learning by Teaching Approach to Help Students Develop Self-Regulatory Skills in Middle School Science Classrooms|
|Principal Investigator:||Biswas, Gautam||Awardee:||Vanderbilt University|
|Program:||Cognition and Student Learning [Program Details]|
|Award Period:||3 years||Award Amount:||$1,499,980|
|Type:||Development and Innovation||Award Number:||R305H060089|
Purpose: Metacognition has been identified as a critical process for supporting students' abilities to solve problems and to learn. Researchers have described two component processes: (a) the ability to monitor one's cognitive activities (e.g., detect comprehension failure), and (b) the ability to take appropriate regulatory steps when a problem has been detected (e.g., slowing down while reading difficult material, asking a teacher for help). Both abilities increase with maturation. At the same time, appropriate educational opportunities can propel the development of metacognition, and thereby improve subsequent learning. The purpose of this project is to develop and test a new form of easily scaled technological support for the development of metacognition, and to examine whether using the metacognitive interventions will be associated with improvement in students' subsequent abilities to learn, in this case the learning of middle school science.
Project Activities: Based on previous research, the researchers hypothesize that helping children learn to monitor other people's problem solving can, in turn, help them monitor their own problem solving and learning. This research will investigate this hypothesis using Teachable Agents (TAs) - software programs in which students teach a computer agent to understand a concept. Using their agent's performance as a motivation, students work to remediate the agent's knowledge, and in the process, they learn better on their own.
Products: The products from this study include a computer-based instructional program for middle school science, and published papers.
Setting: Schools will be located in Tennessee and in California.
Population: In Tennessee, three classes of 5th grade students in a local public school will participate, for a total of about 80 students. In California, the study will involve two 5th-grade classes, or roughly 70 students, who are drawn from the same school and are primarily Hispanic.
Intervention: Two month-long curriculum units about ecosystems will be developed. The Teaching Agent in focus, Betty, is taught using a concept map representation. Students use a point-and-click interface to teach her about entities, such as fish and algae, and their relations (e.g., fish consume dissolved oxygen, algae replenish it). Once taught, Betty uses qualitative reasoning methods to answer questions, such as "if macroinvertebrates increase, what happens to bacteria?" Learning by teaching is implemented as three primary components: (a) teach Betty using a concept map, (b) query Betty with your own questions to see how much she has understood, and (c) quiz Betty with a provided test to see how well she does on questions the student may not have considered. Students reflect on Betty's answers and revise their own knowledge as they make changes to the concept maps to teach Betty better. In the current project, the researchers will investigate Betty's potential for improving student metacognition in the form of self-monitoring and taking actions when warranted.
Research Design and Methods: In Year 1, students will be randomly assigned to one of three conditions: Self-Regulated Learning, Intelligent Tutoring, and Intelligent Tutoring-Self. In the Self-Regulated Learning condition, students will complete lessons in the topic of ecology such that they teach their agent and monitor its performance. In the Intelligent Tutoring condition, students will be told by the Mentor agent to construct a concept map, and the Mentor will provide feedback. In this condition, students do not teach an agent, they are learning for themselves. The Intelligent Tutoring-Self condition serves to test the basic hypothesis that outward monitoring provides a better support for learning to self-monitor than just instructions to self-monitor. Thus, students in the Intelligent Tutoring-Self condition will learn in the same manner as in the Intelligent Tutoring condition, except the mentor will provide metacognition prompts.
Control Condition: The researchers plan to gather benchmark data from 5th-grade classes at other schools who cover the same topics, but are not using the computer intervention.
Key Measures: Conventional measures will be drawn from textbooks and achievement tests (e.g., TCAP), with content consistent with the AAAS Benchmarks for Scientific Literacy. A second type of measure is based on the computer log files of student activity, and include how often students consult quiz questions and content resources, how often they ask for help from the mentor, and how often they test and revise their concept map.
Data Analytic Strategy: This development project is intended only to obtain evidence of the potential efficacy of the intervention. Initial analyses will be at the level of the student.
Related IES Projects: SimSelf: A Simulation Environment Designed to Model and Scaffold Learners' Self-Regulation Skills to Optimize Complex Science Learning (R305A120186)
Hogyeong, J., Gupta, A., Roscoe, R., Wagster, J. Biswas, G., and Schwartz, D. (2008). Using Hidden Markov Models to Characterize Student Behaviors in Learning-By-Teaching Environments. Lecture Notes in Computer Science: Intelligent Tutoring Systems (pp. 614–625). Berlin: Springer.
Schwartz, D.L., Blair, K.P., Biswas, G., Leelawong, K., and Davis, J. (2007). Animations of Thought: Interactivity in the Teachable Agents Paradigm. In R. Lowe, and W. Schnotz (Eds.), Learning With Animation: Research and Implications for Design (pp. 114–140). Cambridge: Cambridge University Press.
Schwartz, D.L., Chase, C., Wagster, J., Okita, S., Roscoe, R., Chin, D., and Biswas, G. (2009). Interactive Metacognition: Monitoring and Regulating a Teachable Agent. In D.J. Hacker, J. Dunlosky, and A.C. Graesser (Eds.), Handbook of Metacognition in Education (pp. 340–358). New York: Routledge.
Journal article, monograph, or newsletter
Blair, K., Schwartz, D.L., Biswas, G., and Leelawong, K. (2007). Pedagogical Agents for Learning by Teaching: Teachable Agents. Educational Technology, 47(1): 56–61.
Chase, C., Chin, D.B., Oppezzo, M., and Schwartz, D.L. (2011). Teachable Agents and the Protege Effect: Increasing the Effort Towards Learning. International Journal of Science Education and Technology, 18(4): 334–352.
Chin, D.B., Dohmen, I.M., Cheng, B.H., Oppezzo, M.A., Chase, C.C., and Schwartz, D.L. (2010). Preparing Students for Future Learning With Teachable Agents. Educational Technology Research and Development, 58(6): 649–669.
Leelawong, K., and Biswas, G. (2008). Designing Learning by Teaching Agents: The Betty's Brain System. International Journal of Artificial Intelligence in Education, 18(3): 181–208.
Lindgren, R., and Schwartz, D.L. (2009). Spatial Learning and Computer Simulations in Science. International Journal of Science Education, 31(3): 419–438.
Roscoe, R.D., Segedy, J.R., Sulcer, B., Jeong, H., and Biswas, G. (2013). Shallow Strategy Development in a Teachable Agent Environment Designed to Support Self-Regulated Learning. Computers & Education, 62: 286–297.
Biswas, G., Schwartz, D., and Catley, K.M. (2008). Enhancing Learning Using Adaptive Computerized Tutoring in K–12 Settings. In B.C. Love, K. McRae, and V.M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 695–696). Washington, DC: Cognitive Science Society.
Jeong, H., and Biswas, G. (2008). Mining Student Behavior Models in Learning-By-Teaching Environments. In Proceedings of the 1st International Conference on Educational Data Mining (pp. 127–136). Montreal, Canada.