Abstract
This chapter explores ways to help students monitor and regulate their learning of difficult chemistry concepts. Dynamic visualizations can illustrate complex, unobservable phenomena such as bond breaking and bond formation. To develop robust, integrated understanding when learning with visualizations, students need cognitive understanding of the phenomena as represented in the visualization. They also need metacognitive skills to decide whether they understand the visualization and determine when to revisit the visualization to clarify their interpretations. We investigate the development of integrated understanding using the Technology-Enhanced Learning in Science (TELS) chemical reactions inquiry unit that combines the pedagogical support of the Web-based Inquiry Science Environment (WISE) with dynamic visualizations from Molecular Workbench. Our first study combining judgments of learning and explanation prompts revealed that visualizations may fail to add new ideas because they are often deceptively clear. Students typically overestimated their understanding of visualizations while gaining only superficial ideas. In our second study we refined both cognitive and metacognitive guidance to encourage students to distinguish and reflect upon their ideas. The results suggest that strengthening self-monitoring skills can overcome deceptive clarity and lead to coherent understanding. These studies suggest that the metacognitive skills of monitoring understanding of complex visualizations and determining when to return to the visualization contribute to the development of integrated understanding and can be supported by careful design of technology-enhanced instruction. The notion of metacognition applied in this study refers to monitoring and evaluating one’s understanding, to the regulation/control function of metacognition, and to the self-knowledge functions of metacognition.
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 subscriptionsReferences
Ainsworth, S., & Loizou, A. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27, 669–681.
Aleven, V., & Koedinger, K. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179.
Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40(4), 199–209.
Azevedo, R., Guthrie, J. T., & Seibert, D. (2005). The role in self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30(2), 87–111.
Baker, L., & Brown, A. L. (1984). Metacognitive skills and reading. In D. Pearson, M. Kamil, R. Barr, & P. Mosenthal (Eds.), Handbook of reading research. New York: Longman.
Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in “gaming the system” behavior in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185–224.
Ben-Zvi, R., Eylon, B.-S., & Silberstein, J. (1987). Students’ visualization of a chemical reaction. Education in Chemistry, 24(4), 117–120.
Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221–252.
Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185–205). Cambridge: MIT Press.
Bjork, R. A., & Linn, M. C. (2006). The science of learning and the learning of science: Introducing desirable difficulties. APS Observer, 19, 29.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn: Brain, mind, experience and school. Washington, DC: National Research Council.
Brown, A. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 60–108). Hillsdale: Erlbaum.
Buckley, B. C., Gobert, J. D., Kindfield, A. C. H., Horwitz, P., Tinker, R. F., Gerlits, B., Wilensky, U., Dede, C., & Willett, J. (2004). Model-based teaching and learning with Biologica: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology, 13(1), 23–41.
Chang, H.-Y., Quintana, C., & Krajcik, J. (2010). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94(1), 73–94.
Chi, M. T. H., Bassok, M., Lewis, M., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.
Chi, M. T. H., De Leew, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.
Chiu, J. L. (2010). Supporting students’ knowledge integration with technology-enhanced inquiry curricula (Doctoral dissertation). Available from Dissertation and Theses database. (UMI No. AAT 3413337).
Clark, D. B., Varma, K., McElhaney, K., & Chiu, J. L. (2008). Structure and design rationale within TELS projects to support knowledge integration. In D. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 157–193). Charlotte: Information Age Publishing.
Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator (Winter), 6–11, 38–46.
Corliss, S., & Spitulnik, M. (2008). Student and teacher regulation of learning in technology-enhanced science instruction. In International Perspectives in the Learning Sciences: Cre8ting a Learning World. Proceedings of the 8th International Conference of the Learning Sciences (Vol. 1, pp. 167–174). Utrecht: International Society of the Learning Sciences, Inc.
Davis, E. A. (2003). Prompting middle school science students for productive reflection: Generic and directed prompts. Journal of the Learning Sciences, 12, 91–142.
Davis, E. A., & Linn, M. C. (2000). Scaffolding students’ knowledge integrations: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819–837.
diSessa, A. (1988). Knowledge in pieces. In G. Forman & P. Pufall (Eds.), Constructivism in the computer age (pp. 49–70). Hillsdale: Lawrence Erlbaum Associates.
Dunlosky, J., & Nelson, T. O. (1992). Importance of the kind of cue for judgments of learning (JOL) and the delayed-JOL effect. Memory and Cognition, 20, 374–380.
Flavell, J. H. (1987). Speculations about the nature and development of metacognition. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding. Hillsdale: Lawrence Erlbaum Associates.
Gabel, D. (1999). Improving teaching and learning through chemistry education research: A look to the future. Journal of Chemical Education, 76(4), 548–553.
Georghiades, P. (2004). From the general to the situated: Three decades of metacognition. International Journal of Science Education, 26, 365–383.
Graesser, A. C., McNamara, D. S., & Van Lehn, K. (2005). Scaffolding deep comprehension strategies through point&query, autotutor, and istart. Educational Psychologist, 40(4), 225–234.
Hammer, D., & Elby, A. (2003). Tapping students’ epistemological resources. Journal of the Learning Sciences, 12(1), 53–91.
Hoffler, T., & Leutner, D. (2007). Instructional animations versus static pictures: A meta-analysis. Learning and Instruction, 17, 722–738.
Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, 107(2), 139–155.
Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75–83.
Kaberman, Z., & Dori, Y. J. (2009). Metacognition in chemistry education: Question posing in the case-based computerized learning environment. Instructional Science, 37(5), 403–436.
Kali, Y. (2006). Collaborative knowledge building using the Design Principles Database. International Journal of Computer-Supported Collaborative Learning, 1, 187–201.
Karpicke, J., & Roediger, H. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966–968.
Keil, F. C. (2006). Explanation and understanding. Annual Review of Psychology, 57, 227–254.
Koriat, A. (1997). Monitoring one’s own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology, 126(4), 349–370.
Koriat, A., Sheffer, L., & Ma’ayan, H. (2002). Comparing objective and subjective learning curves: Judgments of learning exhibit increased underconfidence with practice. Journal of Experimental Psychology: General, 131(2), 147–162.
Kozma, R. (2003). The material features of multiple representations and their cognitive and social affordances for science understanding. Learning and Instruction, 13(2), 205–226.
Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34(9), 949–968.
Krajcik, J. (1991). Developing students’ understandings of chemical concepts. In S. Glynn, R. Yeany, & B. Britton (Eds.), The psychology of learning science (pp. 117–147). Hillsdale: Erlbaum.
Lee, H. –S., Linn, M. C., Varma, K., & Liu, L. (2009). How do technology-enhanced inquiry science units impact classroom learning? Journal of Research in Science Teaching, 47(1), 71–90.
Linn, M. C. (1995). Designing computer learning environments for engineering and computer science: The scaffolded knowledge integration framework. Journal of Science Education and Technology, 4(2), 103–126.
Linn, M. C. (in press). WISE insights for teaching and learning science. In Christopher Dede & John Richards (Eds.), Digital teaching platforms. New York: Teacher’s College Press.
Linn, M. C., & Eylon, B.-S. (2006). Science education. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed.). Mahwah: Erlbaum.
Linn, M. C., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. New York: Routledge.
Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Mahwah: L. Erlbaum Associates.
Linn, M. C., Davis, E. A., & Eylon, B.-S. (2004). The scaffolded knowledge integration framework for instruction. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 73–83). Mahwah: Erlbaum.
Linn, M. C., Lee, H.-S., Tinker, R., Husic, F., & Chiu, J. L. (2006). Teaching and assessing knowledge integration in science. Science, 313, 1049–1050.
Linn, M. C., Chang, H.-Y., Chiu, J., Zhang, H., & McElhaney, K. (2010). Can desirable difficulties overcome deceptive clarity in scientific visualizations? In A. Benjamin (Ed.), Successful remembering and successful forgetting: A Festschrift in honor of Robert A. Bjork (pp. 239–262). New York: Routledge.
Lombrozo, T. (2006). The structure and function of explanations. Trends in Cognitive Sciences, 10(10), 464–470.
Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14, 257–274.
Mathan, S. A., & Koedinger, K. R. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.
Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press.
Mazzoni, G., Cornoldi, C., & Marchitelli, G. (1990). Do memorability ratings affect study-time allocation? Memory and Cognition, 18, 196–204.
McElhaney, K. W. (2010). Making controlled experimentation more informative in inquiry investigations (Doctoral dissertation). Available from Dissertation and Theses database. (UMI No. AAT 3413549).
Minstrell, J. (1992). Facets of students’ knowledge and relevant instruction. In R. Duit, F. Goldberg, & H. Niedderer (Eds.), Research in physics learning: Theoretical issues and empirical studies (pp. 110–128). Kiel: IPN.
Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309–326.
Moreno, R., & Valdez, A. (2005). Cognitive load and learning effects of having students organize pictures and words in multimedia environments: The role of student interactivity and feedback. Educational Technology Research and Development, 53(3), 35–45.
Nakhleh, M. B. (1993). Are our students conceptual thinkers or algorithmic problem solvers? Journal of Chemical Education, 70(1), 52–55.
Nelson, T. O., Dunlosky, J., Graf, A., & Narens, L. (1994). Utilization of metacognitive judgments in the allocation of study during multitrial learning. Psychological Science, 5, 207–213.
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4.
Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and monitoring activities. Cognition and Instruction, 1(2), 117–175.
Pallant, A., & Tinker, R. F. (2004). Reasoning with atomic-scale molecular dynamic models. Journal of Science Education and Technology, 13(1), 51–66.
Quintana, C., Zhang, M., & Krajcik, J. (2005). A framework for supporting metacognitive aspects of online inquiry through software-based scaffolding. Educational Psychologist, 40(4), 235–2244.
Richland, L. E., Linn, M. C., & Bjork, R. A. (2007). Cognition and instruction: Bridging laboratory and classroom settings. In F. Durso, R. Nickerson, S. Dumais, S. Lewandowsky, & T. Perfect (Eds.), Handbook of applied cognition (2nd ed.). New York: Wiley.
Rickey, D., & Stacy, A. (2000). The role of metacognition in chemistry. Journal of Chemical Education, 77(7), 915–920.
Rozenblit, L. R., & Keil, F. C. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26, 521–562.
Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge for the design of new knowledge media. The Journal of Learning Sciences, 1, 37–68.
Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology Research and Development, 53(3), 47–58.
Schoenfeld, A. H. (1985). Mathematical problem solving. New York: Academic.
Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense-making in mathematics. In D. Grouws (Ed.), Handbook for research on mathematics teaching and learning (pp. 334–370). New York: Macmillan.
Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26, 113–125.
Slotta, J., & Linn, M. C. (2009). WISE science: Web-based inquiry in the classroom. New York: Teachers College Press.
Steinkuehler, C., & Duncan, S. (2008). Scientific habits of mind in virtual worlds. Journal of Science Education and Technology, 17(6), 530–543.
Tate, E. (2009). Asthma in the community: Designing instruction to help students explore scientific dilemmas that impact their lives (Doctoral dissertation). Available from ProQuest Dissertation and Theses database. (Umi No. 3383554).
Thiede, K. W., & Dunlosky, J. (1999). Toward a general model of self-regulated study: An analysis of Items for study and self-paced study time. Journal of Experimental Psychology: Learning, Memory and Cognition, 25(4), 1024–1037.
Tien, L., Teichart, M., & Rickey, D. (2007). Effectiveness of a MORE laboratory module in prompting students to revise their molecular-level ideas about solutions. Journal of Chemical Education, 84(1), 175–181.
Tinker, R. (2009). In visualizing to integrate science understanding for all learners (VISUAL), NSF discovery research K-12 grant proposal, #0918743.
Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57, 247–262.
White, B., & Frederiksen, J. (1998). Inquiry, modeling and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.
White, B., & Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40(4), 211–223.
Wiediger, S. D., & Hutchinson, J. S. (2002). The significance of accurate student self-assessment in understanding chemistry concepts. Journal of Chemical Education, 79(1), 120–124.
Williamson, V. M., & Abraham, M. R. (1995). The effects of computer animation on the particulate mental models of college chemistry students. Journal of Research in Science Teaching, 32(5), 521–534.
Wu, H., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38(7), 821–842.
Zahn, C., Barquero, B., & Schwan, S. (2004). Learning with hyperlinked videos – design criteria and efficient strategies for using audiovisual hypermedia. Learning and Instruction, 14(3), 275–291.
Zimmerman, B. (1990). Self-regulating academic learning and achievement: The emergence of a social cognitive perspective. Educational Psychology Review, 2(2), 173–201.
Zoller, U., Fastow, M., Lubezky, A., & Tsaparlis, G. (1999). Students’ self-assessment in chemistry examinations requiring higher- and lower-order cognitive skills. Journal of Chemical Education, 76(1), 112–113.
Acknowledgements
The authors thank the TELS research group, partners, and schools for their dedication to improving science learning. We would also like to thank Sophia Rabe-Hesketh for her help with the analysis. This material is based upon work supported by the National Science Foundation under grant ESI-0242701. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science +Business Media B.V.
About this chapter
Cite this chapter
Chiu, J.L., Linn, M.C. (2012). The Role of Self-monitoring in Learning Chemistry with Dynamic Visualizations. In: Zohar, A., Dori, Y. (eds) Metacognition in Science Education. Contemporary Trends and Issues in Science Education, vol 40. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2132-6_7
Download citation
DOI: https://doi.org/10.1007/978-94-007-2132-6_7
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-2131-9
Online ISBN: 978-94-007-2132-6
eBook Packages: Humanities, Social Sciences and LawEducation (R0)