Instructional Science

, Volume 36, Issue 3, pp 203–231

Exploring the fluctuation of motivation and use of self-regulatory processes during learning with hypermedia

Article

Abstract

We collected think-aloud, pre-test, post-test, and motivation data from 43 undergraduates to examine the impact of conceptual scaffolds on the fluctuation of certain motivation constructs and use of self-regulatory processes during learning with hypermedia. Participants were randomly assigned to either the No Scaffolding (NS) or Conceptual Scaffolding (CS) condition. During the experimental session, each participant individually completed a pre-test on the circulatory system, a pre-task motivation questionnaire, one 30-min hypermedia learning task during which they learned about the circulatory system, a motivation questionnaire at three regular intervals during this learning task, a post-test on the circulatory system, and a post-task motivation questionnaire. Results indicated that while participants in both conditions gained declarative knowledge, participants who received conceptual scaffolds during learning demonstrated deeper understanding of the circulatory system on the post-test. In terms of self-regulatory processes, the results indicated that participants in the CS condition used significantly more planning processes during learning than participants in the NS condition. Additionally, participants in both conditions significantly decreased their use of strategies as they progressed through the learning task. Regarding motivation while learning with hypermedia, results indicated that participants in both conditions reported significantly increased levels of interest as they progressed through the learning task. Furthermore, participants in the CS condition reported the task as being easier and putting forth less effort than participants in the NS condition.

Keywords

Self-regulated learning Hypermedia Motivation Science Scaffold Mixed methods 

References

  1. Anderson, J. R., Corbett, A. T., & Koedinger, K. R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167–207.CrossRefGoogle Scholar
  2. Azevedo, R. (2005). Computer environment as metacogntive tools for enhancing learning. Educational Psychologist, 40(4), 193–197.CrossRefGoogle Scholar
  3. Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96(3), 523–535.CrossRefGoogle Scholar
  4. Azevedo, R., Cromley, J. G., & Seibert, D. (2004a). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia. Contemporary Educational Psychology, 29, 344–370.CrossRefGoogle Scholar
  5. Azevedo, R., Guthrie, J. T., & Seibert, D. (2004b). The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30(1), 87–111.CrossRefGoogle Scholar
  6. Azevedo, R., Winters, F. I., & Moos, D. C. (2004c). Can students collaboratively use hypermedia to lean about science? The dynamics of self- and other-regulatory processes in an ecology classroom. Journal of Educational Computing Research, 31(3), 215–245.CrossRefGoogle Scholar
  7. Azevedo, R., Cromley, J. G., Winters, F. I., Moos, D. C., & Greene, J. A. (2005). Adaptive human scaffolding facilitates adolescents’ self-regulated learning with hypermedia. Instructional Science, 33, 381–412.CrossRefGoogle Scholar
  8. Bandura, A. (1994). Regulative function of perceived self-efficacy. In M. G. Rumsey, C. B. Walker, & J. H. Harris (Eds.) Personnel selection and classification (pp. 261–271). Mahwah, NJ: Erlbaum.Google Scholar
  9. Bergin, D. A., Ford, M. E., & Hess, R. D. (1993). Patterns of motivation and social behavior associated with microcomputer use of young children. Journal of Educational Psychology, 84(3), 272–281.Google Scholar
  10. Boekaerts, M. (2002). The on-line motivation questionnaire: A self-report instrument to assess students’ context sensitivity. New Directions in Measures and Methods, 12, 77–120.Google Scholar
  11. Boekaerts, M., Pintrich, P. R., & Zeidner, M. (Eds.) (2000). Handbook of self-regulation. San Diego, CA: Academic Press. Google Scholar
  12. Chi, M. T. H. (2000). Self-explaining: The dual processes of generating inference and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science (Vol. 5, pp. 161–238). Mahwah, NJ: Erlbaum.Google Scholar
  13. Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14(2), 161–199.CrossRefGoogle Scholar
  14. Chi, M. T. H., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanation improves understanding. Cognitive Science, 18, 439–477.CrossRefGoogle Scholar
  15. Derry S., & Lajoie S. (Eds.) (1993). Computers as cognitive tools. Mahwah, NJ: Erlbaum.Google Scholar
  16. Ericsson, K. A. (2006). Protocol analysis and expert thought: Concurrent verbalizations of thinking during experts’ performance on representative tasks. In K. A. Ericsson, N. Charness, R. R. Hoffman, & P. J. Feltovich (Eds.), The Cambridge handbook of expertise and expert performance (pp. 223–242). Cambridge, MA: Cambridge University Press.Google Scholar
  17. Ericsson, K. A., & Simon, H. A. (1994). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.Google Scholar
  18. Gerjets, P., & Scheiter, K. (2003). Goal configurations and processing strategies as moderators between instructional design and cognitive load: Evidence from hypertext-based instruction. Edcuational Psychologist, 38(1), 33–41.CrossRefGoogle Scholar
  19. Graesser, A. C., McNamara, D. S., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iStart. Educational Psychologist, 40(4), 225–234.Google Scholar
  20. Greene, S., & Ackerman, J. M. (1995). Expanding the constructivist metaphor: A rhetorical perspective on literacy research and practice. Review of Educational Research, 65, 383–420.Google Scholar
  21. Greene, B., & Land, S. (2000). A qualitative analysis of scaffolding use in resource-based learning environments involving the world wide web. Journal of Educational Computing Research, 23(2), 151–179.CrossRefGoogle Scholar
  22. Hadwin, A. F., Boutara, L., & Knoetze, T. (2004). Cross-case study of self-regulated learning as a series of events. Educational Research and Evaluation, 10(4–6), 365–417.CrossRefGoogle Scholar
  23. Hannafin, M., Land, S., & Oliver, K. (1999). Open learning environments: Foundation, methods, and models. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. II, pp. 115–140). Mahwah, NJ: Erlbaum.Google Scholar
  24. Hartley, J., & Sydes, M. (1997). Are structured abstracts easier to read than traditional ones? Journal of Research in Reading, 20(2), 122–136.CrossRefGoogle Scholar
  25. Hill, J. R., & Hannafin, M. (2001). Teaching and learning in digital environments: The resurgence of resource-based learning. Educational Technology Research and Development, 49(3), 37–52.CrossRefGoogle Scholar
  26. Holladay, C. L., & Quiñones, M. A. (2003). Practice variability and transfer of training: The role of self-efficacy generality. Journal of Applied Psychology, 88(6), 1094–1103.CrossRefGoogle Scholar
  27. Jacobson, M., & Archodidou, A. (2000). The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge. Journal of the Learning Sciences, 9(2), 145–199.CrossRefGoogle Scholar
  28. Jonassen, D., Hartley, J., & Trueman, M. (1986). The effects of learner-generated versus text-provided headings on immediate and delayed recall and comprehension: An exploratory study. Human Learning: Journal of Practical Research & Application, 5(3), 139–150.Google Scholar
  29. Jonassen, D., & Reeves, T. (1996). Learning with Technology: Using computers as cognitive tools. In D. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 694–719). NY: Macmillan.Google Scholar
  30. Kester, L., Kirschner, P. A., & van Merriënboer, J. G. (2005). The management of cognitive load during complex cognitive skill acquisition by means of computer-simulated problem solving. British Journal of Educational Psychology, 75(1), 71–85.CrossRefGoogle Scholar
  31. Koedinger, K. R. (2001). Cognitive tutors as modeling tools and instructional models. In K. D.Forbus, & P. J. Feltovich (Eds.). Smart machines in education: The coming revolution in educational technology (pp. 145–167). Cambridge, MA: MIT.Google Scholar
  32. Lajoie S. P. (Ed.) (2000). Computers as cognitive tools: No more walls, Vol. II. Mahwah, NJ: Erlbaum.Google Scholar
  33. Lajoie, S. P. (2005). Cognitive tools for the mind: The promises of technology-cognitive amplifiers or bionic prosthetics? In R. Sternberg, & D. D. Preiss (Eds.), Intelligence and technology: The impact of tools on the nature and development of human abilities. Mahwah, NJ: Erlbaum.Google Scholar
  34. Lajoie, S. P., & Azevedo, R. (2006). Teaching and learning in technology-rich environments. In P. Alexander, & P. Winne (Eds.), Handbook of educational psychology. Mahwah, NJ: Erlbaum.Google Scholar
  35. Land, S., & Greene, B. (2000). Project-based learning with the World Wide Web: A qualitative study of resource integration. Educational Technology Research & Development, 48(3), 61–78.Google Scholar
  36. Lepper, M. R., Woolverton, M., Mumme, D. L., & Gutner, J. L. (1993). Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In S. P. Lajoie, & S. J. Derry (Eds.), Computers as cognitive tools (pp. 75–105). Hillsdale, NJ: Erlbaum.Google Scholar
  37. Lepper, M., & Wolverton, M. (2004). The wisdom of practice: Lessons learned from the study of highly effective tutors. In J. Aranson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 135–158). New York, NY: Academic Press.Google Scholar
  38. Markman, A. B., & Gentner, D. (2000). Structure mapping in the comparison process. American Journal of Psychology, 113(4), 501–538.CrossRefGoogle Scholar
  39. Martin, V. L., & Pressley, M. (1991). Elaborative-interrogation effects depend on nature of question. Journal of Educational Psychology, 83(1), 113–119.CrossRefGoogle Scholar
  40. Mayer, R. E. (1994). Visual aids to knowledge construction: Building mental representations from pictures and words. In W. Schnotz, & R. W. Kulhavy (Eds.), Comprehension of graphics (pp. 125–138). Amsterdam, Netherlands: Elsevier.Google Scholar
  41. Mayer, R. E. (2003). Learning environments: The case for evidence-based practice and issue-driven research. Educational Psychologist, 40, 257–265.Google Scholar
  42. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. CrossRefGoogle Scholar
  43. McCrudden, M. T., Schraw, G., & Kambe, G. (2005). The effect of relevance instructions on reading time and learning. Journal of Educational Psychology, 97(1), 88–102.CrossRefGoogle Scholar
  44. McDaniel, M. A., & Donnelly, C. M. (1996). Learning with analogy and elaborative interrogation. Journal of Educational Psychology, 88(3), 508–519.CrossRefGoogle Scholar
  45. Moos, D. C., & Azevedo, R. (2006). The role of goal structure in undergraduates’ use of self-regulatory variables in two hypermedia learning tasks. Journal of Educational Multimedia and Hypermedia, 15(1), 49–86.Google Scholar
  46. Murphy, K. P., & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25, 3–53.CrossRefGoogle Scholar
  47. Ozgungor, S., & Guthrie, J. T. (2004). Interactions among elaborative interrogation, knowledge, and interest in the process of constructing knowledge form text. Journal of Educational Psychology, 96(3), 437–443.CrossRefGoogle Scholar
  48. Pajares, F. (1996). Self-efficacy beliefs in academic domains. Review of Educational Research, 66(4), 543–578.Google Scholar
  49. Pea, R. D. (2004). The social and technological dimensions of scaffolding and related theoretical concepts for learning, education, and human activity. Journal of the Learning Sciences, 13, 423–451.CrossRefGoogle Scholar
  50. Perry, N. E. (1998). Young children’s self-regulated learning and contexts that support it. Journal of Educational Psychology, 90(4), 715–729.CrossRefGoogle Scholar
  51. Perry, N. E., VandeKamp, K. O., & Mercer, L. K. (2000). Investigating teacher-student interactions that foster self-regulated learning. Educational Psychologist, 37(1), 5–15.Google Scholar
  52. Pintrich, P. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452–502). San Diego, CA: Academic Press.Google Scholar
  53. Pintrich, P. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686.CrossRefGoogle Scholar
  54. Puntambekar, S., & Hubscher, R. (2005). Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist, 40(1), 1–12.CrossRefGoogle Scholar
  55. Seifert, T. L. (1993). Characteristics of ego- and task-orientated students: A comparison of two methodologies. British Journal of Educational Psychology, 65, 125–138.Google Scholar
  56. Shapiro, A. (1999). The relevance of hierarchies to learning biology from hypertext. Journal of the Learning Sciences, 8(2), 215–243.CrossRefGoogle Scholar
  57. Shapiro, A. (2000). The effects of interactive overviews on the development of conceptual structure in novices learning from hypermedia. Journal of Educational Multimedia and Hypermedia, 9(1), 57–78.Google Scholar
  58. Sharp, J. G., Kuerbis, P., & Kelley, G. J. (2006). Children’s ideas about the solar system and the chaos in learning sciences. Science Education, 90(1), 124–147.CrossRefGoogle Scholar
  59. Shute, V., & Psotka, J. (1996). Intelligent tutoring systems: Past, present, and future. In D. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 570–600). NY: Macmillan.Google Scholar
  60. Taylor, R. (1980). The computer in the school: Tutor, tool, tutee. NY: Teachers College Press.Google Scholar
  61. White, B., & Frederiksen, J. (2005). A theoretical framework and approach for fostering metacognitive development. Educational Psychologist, 40(4), 211–223.CrossRefGoogle Scholar
  62. Williams, M. (1996). Student control and instructional technologies. In D. Jonassen (Ed.), Handbook of research on educational communications and technology (pp. 957–983). NY: Macmillan.Google Scholar
  63. Willoughby, T., & Wood, E. (1994). Elaborative interrogation examined at encoding and retrieval. Learning and Instruction, 4(2), 139–149.CrossRefGoogle Scholar
  64. Winne, P. H. (2001). Self-regulated learning viewed from models of information processing. In B. Zimmerman, & D. Schunk. (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 153–189). Mahwah, NJ: Erlbaum.Google Scholar
  65. Winne, P. H. (2005). Key issues in modeling and applying research on self-regulated learning. Applied Psychology: An International Review, 54(2), 232–238.CrossRefGoogle Scholar
  66. Winne, P. H., & Hadwin, A. F. (1998). Studying 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
  67. Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Orlando, FL: Academic Press.Google Scholar
  68. Winne, P. H., & Jamieson-Noel, D. (2003). Self-regulating studying by objectives for learning: Students’ reports compared to a model. Contemporary Educational Psychology, 28(3), 259–276.Google Scholar
  69. Wood, D., & Middleton, D. (1975). A study of assisted problem-solving. British Journal of Psychology, 66(2), 181–191.Google Scholar
  70. Wood, D., Bruner, J., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology & Psychiatry & Allied Disciplines, 17(2), 89–102.CrossRefGoogle Scholar
  71. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 1–37). San Diego, CA: Academic Press.Google Scholar
  72. Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. Zimmerman, & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (pp. 1–37). Mahwah, NJ: Erlbaum.Google Scholar
  73. Zimmerman, B., & Tsikalas, K. (2005). Computer-based learning environments (CBLEs) be used as self-regulatory tools to enhance learning? Educational Psychologist, 40(4), 267–271.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of EducationGustavus Adolphus CollegeSaint PeterUSA
  2. 2.Department of Psychology, Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

Personalised recommendations