David A. Joyner
ABSTRACT
This paper presents the design and evaluation of a set of intelligent tutoring agents constructed to teach teams of students an authentic process of inquiry-driven modeling. The paper first presents the theoretical grounding for inquiry-driven modeling as both a teaching strategy and a learning goal, and then presents the need for guided instruction to improve learning of this skill. However, guided instruction is difficulty to provide in a one-to-many classroom environment, and thus, this paper makes the case that interaction with a metacognitive tutoring system can help students acquire the skill. The paper then describes the design of an exploratory learning environment, the Modeling and Inquiry Learning Application (MILA), and an accompanying set of metacognitive tutors (MILA--T). These tools were used in a controlled experiment with 84 teams (237 total students) in which some teams received and interacted with the tutoring system while other teams did not. The effect of this experiment on teams' demonstration of inquiry-driven modeling are presented.
- Aleven, V., McLaren, B., Roll, I., & Koedinger, K. (2006). Toward metacognitive tutoring: a model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence and Education 16(2), 101--128. Google Scholar
Digital Library
- Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., & Fike, A. (2009). MetaTutor: A MetaCognitive tool for enhancing self-regulated learning. In R. Pirrone, R. Azevedo, & G. Biswas (Eds.), Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems. 14--19.Google Scholar
- Baker, L., & Brown, A. L. (1984). Metacognitive skills and reading. Handbook of reading research, 1, 353--394.Google Scholar
- Biswas, G., Leelawong, K., Schwartz, D., & Vye, N. (2005). Learning By Teaching: A New Agent Paradigm For Educational Software. Applied Artificial Intelligence 19(3-4). 363--392.Google ScholarCross Ref
- Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational psychologist, 26(3-4), 369--398.Google Scholar
- Bransford, J., Brown, A., & Cocking, R. (2000). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press.Google Scholar
- Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of educational research, 65(3), 245--281.Google Scholar
- Clement, J. (2008). Creative Model Construction in Scientists and Students: The Role of Imagery, Analogy, and Mental Simulation. Dordrecht: Springer. Google ScholarDigital Library
- Conati, C., & Vanlehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11, 389--415.Google Scholar
- Crawford, S., & Stucki, L. (1990). Peer review and the changing research record. Journal of the American Society for Information Science, 41(3), 223--228. Google ScholarDigital Library
- Dweck, C. (2000). Self-theories: Their role in motivation, personality, and development. Philadelphia, PA: Psychology Press.Google Scholar
- Edelson, D. (1997). Realizing authentic scientific learning through the adaptation of scientific practice. In K. Tobin & B. Fraser (Eds.), International Handbook of Science Education. Dordrecht, NL: Kluwer.Google Scholar
- Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8(3-4), 391--450.Google ScholarCross Ref
- Gallagher, S. A. (1997). Problem-based learning. Journal for the Education of the Gifted, 20(4), 332--62.Google ScholarCross Ref
- Ghahramani, Z. (2001). An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(01), 9--42.Google ScholarCross Ref
- Gobert, J., Sao Pedro, M., Toto, E., Montalvo, O., & Baker, R. (2011). Science ASSISTments: Assessing and scaffolding students' inquiry skills in real time. Paper presented at The Annual Meeting of the American Educational Research Association, April, 2011, New Orleans, LA.Google Scholar
- Goel, A., Rugaber, S., Joyner, D. A., Vattam, S., Hmelo-Silver, C., Jordan, R., Sinha, S., Honwad, S., & Eberbach, C. (2013). Learning Functional Models of Complex Systems: A Reflection on the ACT project on Ecosystem Learning In Middle School Science. In R. Azevedo & V. Aleven (Eds.) International Handbook on Meta-Cognition and Self-Regulated Learning.Google Scholar
- Griffith, T., Nersessian, N., & Goel, A. (2000). Function-follows-Form: Generative Modeling in Scientific Reasoning. In Proceeds of the 22nd Cognitive Science Conference.Google Scholar
- Jacobs, G. (1992). Hypermedia and discovery-based learning: a historical perspective. British Journal of Educational Technology, 23(2), 113--121.Google ScholarCross Ref
- Joyner, D. (2015). Metacognitive Tutoring for Inquiry-Driven Modeling (Doctoral dissertation). Georgia Institute of Technology, Atlanta, GA.Google Scholar
- Joyner, D. A. & Goel, A. (2014). Attitudinal Gains from Engagement with Metacognitive Tutors in an Exploratory Learning Environment. In Proceedings of the 12th International Conference on Intelligent Tutoring Systems. Honolulu, Hawaii.Google ScholarDigital Library
- Joyner, D. A., Goel, A., & Papin, N. (2014). MILA-S: Generation of agent-based simulations from conceptual models. In Proceedings of the 19th International Conference on Intelligent User Interfaces. Haifa, Israel. 289--298. Google ScholarDigital Library
- Joyner, D. A., Goel, A., Rugaber, S., HmeloSilver, C., & Jordan, R. (2012). Evolution of an Integrated Technology for Supporting Learning about Complex Systems: Looking Back, Looking Ahead. In Proc. Of 11th International Conference on Advanced Learning Technologies, Athens, GA. Google ScholarDigital Library
- Kaberman, Z., & Dori, Y. J. (2009). Question posing, inquiry, and modeling skills of chemistry students in the case-based computerized laboratory environment. International Journal of Science and Mathematics Education, 7(3), 597--625.Google ScholarCross Ref
- Kemeny, J. G., & Snell, J. L. (1960). Finite markov chains (Vol. 356). Princeton, NJ: van Nostrand.Google Scholar
- Ketelhut, D. J. (2007). The impact of student self-efficacy on scientific inquiry skills: An exploratory investigation in River City, a multi-user virtual environment. Journal of Science Education and Technology, 16(1), 99--111.Google ScholarCross Ref
- Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational psychologist, 41(2), 75--86.Google Scholar
- Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction effects of direct instruction and discovery learning. Psychological Science, 15(10), 661--667.Google ScholarCross Ref
- Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Lajoie, S., Lavigne, N., Guerrera, C., & Munsie, S. (2001). Constructing knowledge in the context of Bio World. Instructional Science, 29, 155--186.Google ScholarCross Ref
- López, G. G. I., Hermanns, H., & Katoen, J. P. (2001). Beyond memoryless distributions: Model checking semi-Markov chains. In Process Algebra and Probabilistic Methods. Performance Modelling and Verification (pp. 57--70). Springer Berlin Heidelberg. Google ScholarDigital Library
- Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59(1), 14.Google ScholarCross Ref
- National Research Council. (1996). National science education standards. Washington, DC: National Academy Press.Google Scholar
- Nersessian, N. (1999). Model-based reasoning in conceptual change. In L. Magnani, N. Nersessian, & P. Thagard (Eds.), Model-based reasoning in scientific discovery. New York: Kluwer/Plenum Publishers.Google ScholarCross Ref
- Nersessian, N. (2008). Creating Scientific Concepts. Cambridge, MA: MIT Press.Google ScholarCross Ref
- Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and instruction, 1(2), 117--175.Google Scholar
- Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books. Google ScholarDigital Library
- Piaget, J. (1950). The Psychology of Intelligence. New York: Routledge.Google Scholar
- Razzouk, R. & Shute, V. J. (2012). What is design thinking and why is it important? Review of Educational Research, 82(3), 330--348.Google ScholarCross Ref
- Roll, I., Aleven, V., McLaren, B., & Koedinger, K. (2007). Designing for metacognition - applying cognitive tutor principles to the tutoring of help seeking. Metacognition in Learning 2(2).Google Scholar
- Roll, I., Aleven, V., & Koedinger, K. R. (2010). The invention lab: Using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. In V. Aleven, J. Kay, & J. Mostow (Eds.), In Proceedings of the international conference on intelligent tutoring systems. 115--24. Berlin: Springer Verlag. Google ScholarDigital Library
- Schmidt, H. G. (1998). Problem-based learning: Does it prepare medical students to become better doctors? The Medical Journal of Australia 168. 429--430.Google Scholar
- Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction, 23(2), 165--205.Google ScholarCross Ref
- Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., ... & Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632--654.Google ScholarCross Ref
- Schweingruber, H. A., Duschl, R. A., & Shouse, A. W. (Eds.). (2007). Taking Science to School:: Learning and Teaching Science in Grades K-8. National Academies Press.Google Scholar
- Soloway, E., Pryor, A. Z., Krajcik, J. S., Jackson, S., Stratford, S. J., Wisnudel, M., & Klein, J. T. (1997). ScienceWare's Model-It: Technology to Support Authentic Science Inquiry. Technological Horizons in Education, 25(3), 54--56.Google Scholar
- Ting, C. Y., Zadeh, M. R. B., & Chong, Y. K. (2006, January). A decision-theoretic approach to scientific inquiry exploratory learning environment. In Intelligent Tutoring Systems (pp. 85--94). Springer Berlin Heidelberg. Google ScholarDigital Library
- van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsbergh, E. R., & Manlove, S. (2005). CoLab: Research and development of an online learning environment for collaborative scientific discovery learning. Computers in Human Behaviors 21. 671--688. Google ScholarDigital Library
- VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. B. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3), 209--249.Google ScholarCross Ref
- Vattam, S., Goel, A. K., Rugaber, S., Hmelo-Silver, C., Jordan, R., Gray, S., & Sinha, S. (2011). Understanding Complex Natural Systems by Articulating Structure-Behavior-Function Models. Educational Technology & Society, Special Issue on Creative Design, 14(1), 66--81.Google Scholar
- Weinert, F. E. (1987). Introduction and overview: Metacognition and motivation as determinants of effective learning and understanding. Metacognition, motivation, and understanding. 1--16.Google Scholar
- White, B. & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1). 3--118.Google ScholarCross Ref
- Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann. Google ScholarDigital Library
Index Terms
- Improving Inquiry-Driven Modeling in Science Education through Interaction with Intelligent Tutoring Agents
Recommendations
The Design of Authentic Inquiry for Online Knowledge-Constructive Interaction and Self-Regulated Learning Processes
This study examined students' self-regulated learning processes and satisfaction within an authentic, inquiry-based learning module in a graduate-level online course. In this design-based case study, a WebQuest-based, authentic learning module was ...
Inquiry-Based Learning in Computer Science Classroom
Informatics in Schools. New Ideas in School InformaticsAbstractInquiry-based learning in Slovak schools is still considered to be an innovative approach to teaching based on the active exploration of new knowledge by pupils themselves. It allows deeper involvement of pupils in the learning process, encourages ...
Abductive science inquiry using mobile devices in the classroom
Recent advancements in digital technology have attracted the interest of educators and researchers to develop technology-assisted inquiry-based learning environments in the domain of school science education. Traditionally, school science education has ...
Comments