Skip to main content

Advertisement

Log in

The design of technology-rich learning environments as metacognitive tools in history education

  • Published:
Instructional Science Aims and scope Submit manuscript

Abstract

Research has shown that learners do not always engage in appropriate metacognitive and self-regulatory processes while learning complex historical topics. However, little research exists to guide the design of technology-rich learning environments as metacognitive tools in history education. In order to address this issue, we designed a metacognitive tool using a bottom-up approach. Thirty-two undergraduate students read an historical narrative text either with or without the benefit of the metacognitive tool. Results from process and product data suggest that learners using the metacognitive tool had better recall and that the tool helped them (a) notice that particular events in an historical narrative text are unexplained, and (b) generate hypothetical causes to explain the occurrence of such events. We discuss the implications of these findings for the development of the MetaHistoReasoning Tool, a technology-rich learning environment that assist learners in terms of regulating their learning while they accomplish authentic tasks of historical inquiry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Since our research question required us to compare both the control and treatment conditions in terms of the frequency of each metacognitive variable, we chose a non-parametric test called the Mann–Whitney U (i.e., the non-parametric equivalent to the independent samples t-test). We chose this test due to the non-normal distribution of our data, which is addressed since each score is transformed as a rank. The Mann–Whitney U test compares the mean rank across the control and treatment conditions. This allows us to infer whether the frequencies of each metacognitive variable differed across the groups.

  2. One of the reviewers of this manuscript suggests that this finding may be due to the reminders given to participants to verbalize their thought processes. These reminders were given after the participant fell silent for 3 s, which is more likely to elicit automatic as opposed to strategic processes that are consciously invoked.

References

  • Ainsworth, S., & Burcham, S. (2007). The impact of text coherence on learning by self-explanation. Learning and Instruction, 17, 286–303.

    Google Scholar 

  • Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2010). Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educational Psychologist, 45(4), 224–233.

    Google Scholar 

  • Aulls, M. W., & Shore, B. M. (2008). Inquiry in education (vol. 1): The conceptual foundations for research as a curricular imperative. New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • Azevedo, R. (2005a). Computer environments as metacognitive tools for enhancing learning. Educational Psychologist, 40, 193–198.

    Google Scholar 

  • Azevedo, R. (2005b). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40(4), 199–209.

    Google Scholar 

  • Azevedo, R. (2008). The role of self-regulation in learning about science with hypermedia. In D. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 127–156). Charlotte, NC: Information Age Publishing.

    Google Scholar 

  • Azevedo, R., & Feyzi-Behnagh, R. (2010). Dysregulated learning with advanced learning technologies. Paper presented at the Fall Symposium of the Association for the Advancement of Artificial Intelligence (AAAI), Arlington, Virginia.

  • Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010a). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In M. Khine & I. Saleh (Eds.), New science of learning: Computers, cognition, and collaboration in education. Amsterdam: Springer.

    Google Scholar 

  • Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010b). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45(4), 210–223.

    Google Scholar 

  • Azevedo, R., & Witherspoon, A. M. (2009). Self-regulated learning with hypermedia. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 319–339). Mahwah, NJ: Routledge.

    Google Scholar 

  • Azevedo, R. Witherspoon, A., Chauncey, A., Burkett, C., & Fike, A. (2009, November). MetaTutor: A MetaCognitive tool for enhancing self-regulated learning. Paper presented at the annual meeting of the American Association for Artificial Intelligence, symposium on metacognitive and cognitive educational systems, Washington, DC.

  • Azevedo, R., Witherspoon, A., Graesser, A., McNamara, D., Chauncey, A., Siler, E., et al. (2009). MetaTutor: Analyzing self-regulated learning in a tutoring system for biology. In V. Dimitrova, R. Mizoguchi, B. du Boulay, & A. Graesser (Eds.), Building learning systems that care: From knowledge representation to affective modeling (pp. 635–637). Amsterdam: IOS Press.

    Google Scholar 

  • Azevedo, R., Witherspoon, A., Graesser, A., McNamara, D., Rus, V., Cai, Z., & Lintean, M. (2008). MetaTutor: An adaptive hypermedia system for training and fostering self-regulated learning about complex science topics. Paper presented at annual meeting of the Society for Computers in Psychology, Chicago, IL.

  • Bannert, M., & Mengelkamp, C. (2008). Assessment of metacognitive skills by means of instruction to think-aloud and reflect when prompted. Does the verbalisation method affect learning? Metacognition and Learning, 3, 39–58.

    Google Scholar 

  • Berson, M. J. (1996). Effectiveness of computer technology in social studies: A review of the literature. Journal of Research on Computing in Education, 28(4), 486–499.

    Google Scholar 

  • Berson, M. J., & Balyta, P. (2004). Technological thinking and practice in the social studies: Transcending the tumultuous adolescence of reform. Journal of Computing in Teacher Education, 20(4), 141–150.

    Google Scholar 

  • Bolick, C. M. (2004). The giant is waking! Journal of Computing in Teacher Education, 20(4), 130–132.

    Google Scholar 

  • Bolick, C. M., Berson, M. J., Coutts, C., & Heinecke, W. (2003). Technology applications in social studies teacher education: A survey of social studies methods faculty. Contemporary Issues in Technology and Teacher Education, 3(3), 300–309.

    Google Scholar 

  • Bolick, C. M., Berson, M. J., Friedman, A. M., & Porfeli, E. J. (2007). Diffusion of technology innovation in the preservice social studies experience: Results of a national survey. Theory and Research in Social Education, 35(2), 174–195.

    Google Scholar 

  • Bolick, C. M., McGlinn, M. M., & Siko, K. L. (2006). Twenty years of technology: A retrospective view of Social Education’s technology themed issues. Social Education, 69(3), 155–161.

    Google Scholar 

  • Boonthum, C., Levinstein, I. B., & McNamara, D. S. (2007). Evaluating self-explanations in iSTART: Word matching, latent semantic analysis, and topic models. In A. Kao & S. Poteet (Eds.), Text mining and natural language processing. London: Springer.

    Google Scholar 

  • Britt, M. A., & Aglinskas, C. (2002). Improving student’s ability to use source information. Cognition & Instruction, 20, 485–522.

    Google Scholar 

  • Britt, M. A., Perfetti, C. A., van Dyke, J., & Gabrys, G. (2000). The sourcer’s apprentice: A tool for document-supported history instruction. In P. Stearns, P. Seixas, & S. Wineburg (Eds.), Knowing, teaching and learning history: National and international perspectives (pp. 437–470). New York: New York University Press.

    Google Scholar 

  • Britt, M. A., Rouet, J.-F., Georgi, M. C., & Perfetti, C. A. (1994). Learning from history texts: From causal analysis to argument models. In G. Leinhardt, I. L. Beck, & C. Stainton (Eds.), Teaching and learning in history (pp. 47–84). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Britt, M. A., Rouet, J. F., & Perfetti, C. A. (1996). Using hypertext to study and reason about historical evidence. In J. F. Rouet, J. Levonen, A. Dillon, & R. Spiro (Eds.), Hypertext and cognition (pp. 43–72). Mahwah, NJ: LEA.

    Google Scholar 

  • Britt, M. A., Wiemer-Hastings, P., Larson, A. A., & Perfetti, C. A. (2004). Using intelligent feedback to improve sourcing and integration in students’ essays. International Journal of Artificial Intelligence in Education, 14, 359–374.

    Google Scholar 

  • Brush, T. A., & Saye, J. W. (2000). Implementation and evaluation of a student-centered learning unit: A case study. Educational Technology Research and Development, 38(3), 79–100.

    Google Scholar 

  • Brush, T. A., & Saye, J. W. (2001). The use of embedded scaffolds with hypermedia-supported student-centered learning. Journal of Educational Multimedia and Hypermedia, 10(4), 333–356.

    Google Scholar 

  • Brush, T. A., & Saye, J. W. (2002). A summary of research exploring hard and soft scaffolding for teachers and students using a multimedia supported learning environment. Journal of Interactive Online Learning, 1(2), 1–12.

    Google Scholar 

  • Brush, T. A., & Saye, J. W. (2004). Supporting learners in technology-enhanced student-centred learning environments. International Journal of Learning Technology, 1(2), 191–202.

    Google Scholar 

  • Callender, A. A., & McDaniel, M. A. (2007). The benefits of embedded question adjuncts for low and high structure builders. Journal of Educational Psychology, 99, 339–348.

    Google Scholar 

  • Carretero, M., López-Manjón, A., & Jacott, L. (1997). Explaining historical events. International Journal of Educational Research, 27(3), 245–253.

    Google Scholar 

  • Carretero, M., & Voss, J. F. (1994). Cognitive and instructional processes in history and the social sciences. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Dinsmore, D., Alexander, P., & Loughlin, S. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20(4), 391–409.

    Google Scholar 

  • Doolittle, P., & Hicks, D. (2003). Constructivism as a theoretical foundation for the use of technology in Social Studies. Theory and Research in Social Education, 31(1), 72–104.

    Google Scholar 

  • Dornisch, M. M., & Sperling, R. A. (2004). Elaborative questions in web-based text materials. International Journal of Instructional Media, 31(1), 59–69.

    Google Scholar 

  • Dornisch, M. M., & Sperling, R. A. (2006). Facilitating learning from technology-enhanced text: Effects of prompted elaborative interrogation. Journal of Educational Research, 99(3), 156–166.

    Google Scholar 

  • Ehman, L., & Glenn, A. (1991). Interactive technology in the social studies. In J. P. Shaver (Ed.), Handbook of research on social studies teaching and learning (pp. 513–522). New York, NY: Macmillan Publishing.

    Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data. Cambridge, MA: The MIT Press. revised edition.

    Google Scholar 

  • Eslinger, E., White, B., & Frederiksen, J. (2001). A modifiable multi-agent system for supporting inquiry learning. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.), Artificial intelligence in education. Amsterdam: IOS Press.

    Google Scholar 

  • Eslinger, E., White, B., Frederiksen, J., & Brobst, J. (2008). Supporting inquiry processes with an interactive learning environment: Inquiry Island. Journal of Science Education and Technology, 17, 610–617.

    Google Scholar 

  • Espin, C. A., Cevasco, J., van den Broek, P., Baker, S., & Gersten, R. (2007). History as narrative: The nature and quality of historical understanding for students with learning disabilities. Journal of Learning Disabilities, 40, 174–182.

    Google Scholar 

  • Flavell, J. H. (1979). Metacognitive aspects of problem solving. In L. B. Resnick (Ed.), The nature of intelligence (pp. 231–236). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Friedman, A. M., & Hicks, D. (2006). The state of the field: Technology, social studies, and teacher education. Contemporary Issues in Technology and Teacher Education, 6(2), 246–258.

    Google Scholar 

  • Gilabert, R., Martínez, G., & Vidal-Abarca, E. (2005). Some good texts are always better: Text revision to foster inferences of readers with high and low prior background knowledge. Learning and Instruction, 15(1), 45–68.

    Google Scholar 

  • Golding, J. M., Graesser, A. C., & Millis, K. K. (1990). What makes a good answer to a question?: Testing a psychological model of question answering. Discourse Processes, 13, 305–325.

    Google Scholar 

  • Graesser, A. C., Baggett, W., & Williams, K. (1996). Question-driven explanatory reasoning. Applied Cognitive Psychology, 10, 17–32.

    Google Scholar 

  • Graesser, A. C., & Franklin, S. P. (1990). QUEST: A cognitive model of question answering. Discourse Processes, 13(2), 279–303.

    Google Scholar 

  • Graesser, A. C., Lang, K. L., & Roberts, R. M. (1991). Question answering in the context of stories. Journal of Experimental Psychology: General, 120, 254–277.

    Google Scholar 

  • Graesser, A. C., McNamara, D. S., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist, 40, 225–234.

    Google Scholar 

  • Graesser, A. C., Singer, M., & Trabasso, T. (1994). Constructing inferences during narrative text comprehension. Psychological Review, 101, 371–395.

    Google Scholar 

  • Greene, J. A., Bolick, C. M., & Robertson, J. (2010). Fostering historical knowledge and thinking skills using hypermedia learning environments: The role of self-regulated learning. Computers & Education, 54, 230–243.

    Google Scholar 

  • Hacker, D. J. (1998). Self-regulated comprehension during normal reading. In D. J. Hacker, J. Dunlosky, & A. C. Graeser (Eds.), Metacognition in educational theory and practice (pp. 165–191). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Hicks, D., & Doolittle, P. E. (2008). Fostering analysis in historical inquiry through multimedia embedded scaffolding. Theory and Research in Social Education, 36(3), 206–232.

    Google Scholar 

  • Hicks, D., & Doolittle, P. E. (2009). Multimedia-based historical inquiry strategy instruction. Do size and form really matter? In J. Lee & A. Friedman (Eds.), Research on technology in social studies education. Greenwich, CT: Information Age Publishing.

    Google Scholar 

  • Hicks, D., Doolittle, P. E., & Ewing, T. (2004). The SCIM-C strategy: Expert historians, historical inquiry, and multimedia. Social Education, 68(3), 221–225.

    Google Scholar 

  • Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42, 99–107.

    Google Scholar 

  • Jonassen, D. H. (1996). Computers in the classroom: Mindtools for critical thinking. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Jonassen, D. H. (2003). Using cognitive tools to represent problems. Journal of Research in Technology in Education, 35(3), 362–381.

    Google Scholar 

  • Jonassen, D. H., & Reeves, T. (1996). Learning with technology: Using computers as cognitive tools. In D. Jonassen (Ed.), Handbook of research on educational communication and technology (pp. 693–719). New York: Scholastic.

    Google Scholar 

  • Kendeou, P., & van den Broek, P. (2005). The effects of readers’ misconceptions on comprehension of scientific text. Journal of Educational Psychology, 97, 235–245.

    Google Scholar 

  • Kendeou, P., & van den Broek, P. (2007). Interactions between prior knowledge and text structure during comprehension of scientific texts. Memory & Cognition, 35, 1567–1577.

    Google Scholar 

  • 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, 75–86.

    Google Scholar 

  • Kommers, P., Jonassen, D. H., & Mayes, T. (Eds.). (1992). Cognitive tools for learning. Heidelberg, FRG: Springer.

    Google Scholar 

  • Lajoie, S. P. (Ed.). (2000). Computers as cognitive tools (Vol. 2): No more walls. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Lajoie, S. P. (2005). Cognitive tools for the mind: The promises of technology: Cognitive amplifiers or bionic prosthetics? In R. J. Sternberg & D. Preiss (Eds.), Intelligence and technology: Impact of tools on the nature and development of human skills (pp. 87–102). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Lajoie, S. P. (2007). Developing computer based learning environments based on complex performance models. In B. Shuart, W. Spaulding, & J. Poland (Eds.), Nebraska symposium on motivation. Modeling complex systems (Vol. 52, pp. 123–144). Lincoln: University of Nebraska Press.

    Google Scholar 

  • Lajoie, S. P. (2009). Developing professional expertise with a cognitive apprenticeship model: Examples from avionics and medicine. In K. A. Ericsson (Ed.), Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments (pp. 61–83). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Lajoie, S. P., & Azevedo, R. (2006). Teaching and learning in technology-rich learning environments. In P. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 803–821). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Lajoie, S. P., & Derry, S. J. (Eds.). (1993). Computers as cognitive tools. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Lee, J. K., & Friedman, A. M. (2009). More to follow: The untapped research agenda in social studies and technology. In J. Lee & A. M. Friedman (Eds.), Research on technology in social studies education. Charlotte, NC: Information Age Publishing.

    Google Scholar 

  • Lee, J. K., & Hicks, D. (2006). Editorial: Discourse on technology in social education. Contemporary Issues in Technology and Teacher Education, 6(4), 414–417.

    Google Scholar 

  • Leinhardt, G., & Stainton, C. (1994). A sense of history. Educational Psychologist, 29(2), 79–89.

    Google Scholar 

  • Leinhardt, G., & Young, K. M. (1996). Two texts, three readers: Distance and expertise in reading history. Cognition and Instruction, 14, 441–486.

    Google Scholar 

  • Leslie, L., & Caldwell, J. A. (2009). Formal and Informal measures of reading comprehension. In S. E. Israel & G. G. Duffy (Eds.), Handbook of research on reading comprehension (pp. 403–427). New York: Routledge.

    Google Scholar 

  • Levitt, J. (1970). Henri Bourassa on imperialism and biculturalism, 1900–1918. Toronto, CA: Copp Clark Pub Co.

    Google Scholar 

  • Linderholm, T., Everson, M., van den Broek, P., Mischinski, M., Crittenden, A., & Samuels, J. (2000). Effects of causal text revisions on more- and less-skilled readers’ comprehension of easy and difficult texts. Cognition and Instruction, 18, 525–556.

    Google Scholar 

  • Linderholm, T., Virtue, S., Tzeng, Y., & van den Broek, P. (2004). Fluctuations in the availability of information during reading: Capturing cognitive processes using the Landscape Model. Discourse Processes, 37, 165–186.

    Google Scholar 

  • Logtenberg, A., van Boxtel, C., & van Hout-Wolters, B. (2011). Stimulating situational interest and student questioning through three types of historical introductory texts. European Journal of Psychology of Education, 26(2), 179–198.

    Google Scholar 

  • Long, D. L., Golding, J. M., & Graesser, A. C. (1992). The generation of goal related inferences during narrative comprehension. Journal of Memory and Language, 31, 634–647.

    Google Scholar 

  • Magliano, J. P., & Graesser, A. C. (1991). A three-pronged method for studying inference generation in literary text. Poetics, 20, 193–232.

    Google Scholar 

  • Magliano, J. P., Trabasso, T., & Graesser, A. C. (1999). Strategic processing during comprehension. Journal of Educational Psychology, 91(4), 615–629.

    Google Scholar 

  • Martin, V. L., & Pressley, M. (1991). Elaborative-interrogation effects depend on the nature of the question. Journal of Educational Psychology, 83, 113–119.

    Google Scholar 

  • Martorella, P. H. (Ed.). (1997). Interactive technologies and the social studies: Emerging issues and applications. Albany, NY: State University of New York Press.

    Google Scholar 

  • McCrudden, M. T., & Schraw, G. (2007). Relevance and goal-focusing in text processing. Educational Psychology Review, 19(2), 113–139.

    Google Scholar 

  • McDaniel, M., & Donnelly, C. (1996). Learning with analogy and elaborative interrogation. Journal of Educational Psychology, 88, 508–519.

    Google Scholar 

  • McNamara, D. S., Boonthum, C., Levinstein, I. B., & Millis, K. (2007a). Evaluating self-explanations in iSTART: Comparing word-based and LSA algorithms. In T. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of LSA (pp. 227–241). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • McNamara, D. S., O’Reilly, T., Rowe, M., Boonthum, C., & Levinstein, I. B. (2007b). iSTART: A web-based tutor that teaches self-explanation and metacognitive reading strategies. In D. S. McNamara (Ed.), Reading comprehension strategies: Theories, interventions, and technologies (pp. 397–421). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Neisser, U. (1967). Cognitive psychology. New York: Appleton.

    Google Scholar 

  • Nelson, T. O. (1996). Consciousness and metacognition. American Psychologist, 51, 102–116.

    Google Scholar 

  • Nelson, T. O., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 26, pp. 125–141). New York: Academic Press.

    Google Scholar 

  • Nelson, T. O., & Narens, L. (1994). Why investigate metacognition? In J. Metcalfe & A. P. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 1–25). Cambridge, MA: MIT Press.

    Google Scholar 

  • O’Neill, D. K., & Weiler, M. J. (2006). Cognitive tools for understanding history: What more do we need? Journal of educational computing research, 35(2), 181–197.

    Google Scholar 

  • Ozgungor, S., & Guthrie, J. T. (2004). Interactions among elaborative interrogation, knowledge, and interest in the process of constructing knowledge from text. Journal of Educational Psychology, 96, 437–443.

    Google Scholar 

  • Pea, R. D. (1985). Beyond amplification: Using the computer to reorganize mental functioning. Educational Psychologist, 20(4), 167–182.

    Google Scholar 

  • Perkins, D. N. (1985). The fingertip effect: How information processing technology shapes thinking. Educational Researcher, 14, 11–17.

    Google Scholar 

  • Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientations in learning and achievement. Journal of Educational Psychology, 92, 544–555.

    Google Scholar 

  • Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407.

    Google Scholar 

  • Poitras, E. (2010). A metacognitive tool to support reading comprehension of historical narratives. Unpublished manuscript, Department of Educational and Counseling Psychology, McGill University, Montreal, Canada.

  • Pressley, M., & Afflerbach, P. (1995). Verbal protocols of reading: The nature of constructively responsive reading. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Rouet, J.-F., Britt, M. A., Manson, R. A., & Perfetti, C. A. (1996). Using multiple sources of evidence to reason about history. Journal of Educational Psychology, 88, 478–493.

    Google Scholar 

  • Salomon, G., & Perkins, D. (2005). Do technologies make us smarter? Intellectual amplification with and through technology. In R. J. Sternberg & D. D. Preiss (Eds.), Intelligence and technology: The impact of tools on the nature and development of human abilities. NJ: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Salomon, G., Perkins, D., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20, 10–16.

    Google Scholar 

  • Saye, J. W., & Brush, T. (1999). Student engagement with social issues in a multimedia-supported learning environment. Theory and Research in Social Education, 27(4), 472–504.

    Google Scholar 

  • Saye, J. W., & Brush, T. (2002). Scaffolding critical reasoning about history and social issues in multimedia-supported learning environments. Educational Technology Research and Development, 50(3), 77–96.

    Google Scholar 

  • Saye, J. W., & Brush, T. (2004). Promoting civic competence through problem-based history learning experiments. In G. E. Hamot, J. J. Patrick, & R. S. Leming (Eds.), Civic learning in teacher education (Vol. 3, pp. 123–145). Bloomington, IN: The Social Studies Development Center.

    Google Scholar 

  • Saye, J. W., & Brush, T. (2005). The persistent issues in history network: Developing civic competence through technology-supported historical inquiry. Social Education, 69(4), 168–171.

    Google Scholar 

  • Saye, J. W., & Brush, T. (2006). Comparing teachers’ strategies for supporting student inquiry in a problem-based multimedia-enhanced history unit. Theory and Research in Social Education, 34(2), 183–212.

    Google Scholar 

  • Saye, J. W., & Brush, T. (2007). Using technology-enhanced learning environments to support problem-based historical inquiry in secondary school classrooms. Theory and Research in Social Education, 35(2), 196–230.

    Google Scholar 

  • Saye, J. W., & Brush, T. (2009). Using the affordances of technology to develop teacher expertise in historical inquiry. In J. Lee & A. Friedman (Eds.), Research on technology in social studies education. Greenwich, CT: Information Age Publishing.

    Google Scholar 

  • Schraw, G. (2010). Measuring self-regulation in computer-based learning environments. Educational Psychologist, 45(4), 258–266.

    Google Scholar 

  • Schunk, D. H. (2005). Self-regulated learning: The educational legacy of Paul R. Pintrich. Educational Psychologist, 40, 85–94.

    Google Scholar 

  • Seifert, T. L. (1993). Effects of elaborative interrogation with prose passages. Journal of Educational Psychology, 85, 642–651.

    Google Scholar 

  • Seifert, T. L. (1994). Enhancing memory for main ideas using elaborative interrogation. Contemporary Educational Psychology, 19(3), 360–366.

    Google Scholar 

  • Seixas, P. (1993). The community of inquiry as a basis for knowledge and learning: The case of history. American Educational Research Journal, 30(2), 305–324.

    Google Scholar 

  • Shimoda, T. A., White, B. Y., & Frederiksen, J. R. (1999). Acquiring and transferring intellectual skills with modifiable software agents in a virtual inquiry support environment. Proceedings of the 32nd annual Hawai international conference on system sciences. Los Alamitos, CA: IEEE Computer Society.

  • Shimoda, T. A., White, B. Y., & Frederiksen, J. R. (2002). Student goal orientation in learning inquiry skills with modifiable software advisors. Science Education, 86, 244–263.

    Google Scholar 

  • Spoehr, K. T., & Spoehr, L. W. (1994). Learning to think historically. Educational Psychologist, 29, 71–77.

    Google Scholar 

  • Swan, K. O., & Hofer, M. (2008). Technology and social studies. In L. Levstik & C. A. Tyson (Eds.), Handbook of research in social studies education (pp. 307–326). New York: Sage.

    Google Scholar 

  • The Psychological Corporation. (2007). Weschler fundamentals: Academic skills-Canadian. Toronto, ON: Pearson Canada Assessment Inc.

  • Thieman, G. Y. (2008). Using technology as a tool for learning and developing 21st century citizenship skills: An examination of the NETS and technology use by preservice teachers with their K-12 students. Contemporary Issues in Technology and Teacher Education, 8(4), 342–366.

    Google Scholar 

  • Trabasso, T., Secco, T., & van den Broek, P. W. (1984). Causal cohesion and story coherence. In H. Mandl, N. L. Stein, & T. Trabasso (Eds.), Learning and comprehension of text (pp. 83–111). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Trabasso, T., & Sperry, L. L. (1985). Causal relatedness and importance of story events. Journal of Memory and Language, 24, 595–611.

    Google Scholar 

  • Trabasso, T., & van den Broek, P. (1985). Causal thinking and the representation of narrative events. Journal of Memory and Language, 24, 612–630.

    Google Scholar 

  • Trabasso, T., van den Broek, P., & Lui, L. (1988). A model for generating questions that assess and promote comprehension. Questioning Exchange, 2, 25–38.

    Google Scholar 

  • Trabasso, T., van den Broek, P., & Suh, S. Y. (1989). Logical necessity and transitivity of causal relations in stories. Discourse Processes, 12, 1–25.

    Google Scholar 

  • Tzeng, Y., van den Broek, P., Kendeou, P., & Lee, C. (2005). The computational implementation of the Landscape Model: Modeling inferential processes and memory representations of text comprehension. Behavioral Research Methods, Instruments & Computers, 37, 277–286.

    Google Scholar 

  • van den Broek, P., Rapp, D. N., & Kendeou, P. (2005). Integrating memory-based and constructionist approaches in accounts of reading comprehension. Discourse Processes, 39, 299–316.

    Google Scholar 

  • van den Broek, P., Risden, K., Fletcher, C. R., & Thurlow, R. (1996). A “landscape” view of reading: Fluctuating patterns of activation and the construction of a stable memory representation. In B. K. Britton & A. C. Graesser (Eds.), Models of understanding text (pp. 165–187). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • van den Broek, P. W., Risden, K., & Husebye-Hartmann, E. (1995). Comprehension of narrative events: Maintaining sufficient explanations. In R. F. Lorch Jr. & E. O’Brien (Eds.), Sources of coherence in text comprehension (pp. 353–373). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • van den Broek, P., & Trabasso, T. (1986). Causal networks vs. goal hierarchies in summarizing text. Discourse Processes, 9, 1–15.

    Google Scholar 

  • van den Broek, P., Young, M., Tzeng, Y., & Linderholm, T. (1999). The landscape model of reading. In H. van Oostendorp & S. R. Goldman (Eds.), The construction of mental representations during reading (pp. 71–98). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • van Drie, J., & van Boxtel, C. (2008). Historical reasoning: Towards a framework for analyzing students’ reasoning about the past. Educational Psychology Review, 20, 87–110.

    Google Scholar 

  • Vidal-Abarca, E., Gilabert, R., Gil, L., & Martínez, T. (2006). How to make good texts for learning: Reviewing text revision research. In A. V. Mitel (Ed.), Focus on educational psychology. New York: Nova Science Publishers.

    Google Scholar 

  • Vidal-Abarca, E., Martínez, T., & Gilabert, R. (2000). Two procedures to improve instructional text: Effects on memory and learning. Journal of Educational Psychology, 92, 1–10.

    Google Scholar 

  • Voss, J. F., & Wiley, J. (2006). Expertise in history. In K. A. Ericsson, N. Charness, P. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 569–584). Cambridge: Cambridge University Press.

    Google Scholar 

  • White, B., & Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40, 211–223.

    Google Scholar 

  • White, B., Shimoda, T., & Frederiksen, J. (1999). Enabling students to construct theories of collaborative inquiry and reflective learning: Computer support for metacognitive development. International Journal of Artificial Intelligence in Education, 10(2), 151–182.

    Google Scholar 

  • Whitworth, S. A., & Berson, M. J. (2003). Computer technology in the social studies: An examination of the effectiveness literature (1996–2001). Contemporary Issues in Technology and Teacher Education, 2(4), 472–509.

    Google Scholar 

  • Willoughby, T., Wood, E., & Kahn, M. (1994). Isolating variables that impact or detract from the effectiveness of elaboration strategies. Journal of Educational Psychology, 86, 279–289.

    Google Scholar 

  • Winne, P. H., & Hadwin, A. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). San Diego, CA: Academic Press.

    Google Scholar 

  • Wolfe, S., Brush, T., & Saye, J. (2003). Using an information problem-solving model as a metacognitive scaffold for multimedia-supported information-based problems. Journal of Research on Technology in Education, 35(3), 321–341.

    Google Scholar 

  • Woloshyn, V. E., Paivio, A., & Pressley, M. (1994). Use of elaborative interrogation to help students acquire information consistent with prior knowledge and information inconsistent with prior knowledge. Journal of Educational Psychology, 86, 79–89.

    Google Scholar 

  • Woloshyn, V., Pressley, M., & Schneider, W. (1992). Elaborative interrogation and prior knowledge effects on learning of facts. Journal of Educational Psychology, 84, 115–124.

    Google Scholar 

  • Wood, E., Pressley, M., & Winne, P. H. (1990). Elaborative interrogation effects on children’s learning of factual content. Journal of Educational Psychology, 82, 741–748.

    Google Scholar 

  • Zimmerman, B. J. (2000). Attainment of self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Academic Press.

    Google Scholar 

  • Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183.

    Google Scholar 

Download references

Acknowledgments

We would like to acknowledge the funding we received from the Social Sciences and Humanities Research Council of Canada.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eric Poitras.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Poitras, E., Lajoie, S. & Hong, YJ. The design of technology-rich learning environments as metacognitive tools in history education. Instr Sci 40, 1033–1061 (2012). https://doi.org/10.1007/s11251-011-9194-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11251-011-9194-1

Keywords

Navigation