Abstract
Inquiry-based learning has been proposed as a natural and authentic way for students to engage with science. Inquiry-based learning environments typically require students to guide their own learning and inquiry processes as they gather data, make and test hypotheses and draw conclusions. Some students are highly self-regulated learners and are able to guide and monitor their own learning activities effectively. Unfortunately, many students lack these skills and are consequently less successful in open-ended, inquiry-based environments. This work examines differences in inquiry behavior patterns in an open-ended, game-based learning environment, Crystal Island. Differential sequence mining is used to identify meaningful behavior patterns utilized by Low, Medium, and High self-regulated learners. Results indicate that self-regulated learners engage in more effective problem solving behaviors and demonstrate different patterns of use of the provided cognitive tools. The identified patterns help provide further insight into the role of SRL in inquiry-based learning and inform future approaches for scaffolding.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alfieri, L., Brooks, P., Aldrich, N., Tenenbaum, H.: Does Discovery-Based Instruction Enhance Learning. Journal of Education Psychology, 103, 1–18 (2011)
Kirschner, P.A., Sweller, J., Clark, R.E.: 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, 75–86 (2006)
Roll, I., Aleven, V., Koedinger, K.R.: The Invention Lab: Using a Hybrid of Model Tracing and Constraint-Based Modeling to Offer Intelligent Support in Inquiry Environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 115–124. Springer, Heidelberg (2010)
Woolf, B.P., et al.: Critical Thinking Environments for Science Education. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education, pp. 515–522 (2005)
Ketelhut, D.J.: 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, 99–111 (2007)
Land, S.: Cognitive requirements for learning with open-ended learning environments. Educational Technology Research and Development 48, 61–78 (2000)
Schunk, D.H.: Attributions as Motivators of Self-Regulated Learning. In: Motivation and Self-Regulated Learning: Theory, Research, and Applications, pp. 245–266 (2008)
Ellis, D., Zimmerman, B.J.: Enhancing self-monitoring during self-regulated learning of speech, pp. 205–228 (2001)
Kinnebrew, J.S., Loretz, K.M., Biswas, G.: A Contextualized, Differential Sequence Mining Method to Derive Students’ Learning Behavior Patterns. Journal of Educational Data Mining (in press)
Zimmerman, B.J.: Self-regulated learning and academic achievement: An overview. Educational Psychologist 25, 3–17 (1990)
Kostons, D., van Gog, T., Paas, F.: Training Self-Assessment and Task-Selection Skills: A Cognitive Approach to Improving Self-Regulated Learning. Learning and Instruction 22, 121–132 (2012)
Cuevas, P., Lee, O., Hart, J., Deaktor, R.: Improving Science Inquiry with Elementary Students of Diverse Backgrounds. Journal of Research in Science Teaching 42, 337–357 (2005)
Azevedo, R., Cromley, J.G., Winters, F.I., Moos, D.C., Greene, J.A.: Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional Science 33, 381–412 (2005)
Biswas, G., Jeong, H., Roscoe, R., Sulcer, B.: Promoting Motivation and Self-Regulated Learning Skills through Social Interactions in Agent-Based Learning Environments. In: 2009 AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems (2009)
Sabourin, J., Shores, L.R., Mott, B.W., Lester, J.C.: Predicting Student Self-regulation Strategies in Game-Based Learning Environments. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 141–150. Springer, Heidelberg (2012)
Rowe, J.P., Shores, L.R., Mott, B.W., Lester, J.C.: Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments. International Journal of Artificial Intelligence in Education, 166–177 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sabourin, J., Mott, B., Lester, J. (2013). Discovering Behavior Patterns of Self-Regulated Learners in an Inquiry-Based Learning Environment. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-39112-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
eBook Packages: Computer ScienceComputer Science (R0)