An Instrumentalized Framework for Supporting Learners’ Self-regulation in Blended Learning Environments

  • Stijn Van LaerEmail author
  • Jan Elen
Living reference work entry


The premise in instructional design theory is that, in order to identify and target instructional shortcomings, designers should conduct a thorough analysis of the various elements involved in the instructional process. This is also the case for technology-rich means of instruction such as online and blended learning. Nevertheless it often seems that insufficient attention is directed to the description of learning environments when redesigning them. In the case of blended learning, studies suggest, for example, that this type of learning often challenges learners’ self-regulation. Existing research provides little insight into how blended environments can support learners’ self-regulation. These observations are problematic since such insights are needed for effective (re)designs. Therefore, the aim of this chapter is to present an instrumentalized framework which can be used to describe and thus characterize support for learners’ self-regulation in blended learning environments as a basis for investigations and empirical trials to uncover effective redesigns and guidelines. The instrumentalized framework elaborates on seven attributes of learning environments that may be expected to support self-regulation according to the current literature on self-regulation. The framework is operationalized in an instrument that facilitates the description of any blended learning environment from the perspective of learners’ self-regulation support. We demonstrate the validity and reliability of the instrument in two empirical research cycles which included six blended learning environments. The instrument can be used to describe and characterize environments as a starting point for their redesign and, consequently, improve support for self-regulation.


Self-regulation Instructional design Blended learning Descriptive instrument Design guidelines 



We would like to acknowledge the support of Bicol University and the project “Adult Learners Online,” financed by the Agency for Science and Technology (Project Number: SBO 140029), which made this research possible.


  1. Andersson, B., & Bach, F. (2005). On designing and evaluating teaching sequences taking geometrical optics as an example. Science Education, 89(2), 196–218. Scholar
  2. Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition–implications for the design of computer-based scaffolds. Instructional Science, 33(5), 367–379.CrossRefGoogle Scholar
  3. Bandura, A. (1993). Perceived self-efficacy in cognitive-development and functioning. Educational Psychologist, 28(2), 117–148. Scholar
  4. Bannert, M., Sonnenberg, C., Mengelkamp, C., & Pieger, E. (2015). Short-and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Computers in Human Behavior, 52, 293–306.CrossRefGoogle Scholar
  5. Boelens, R., Van Laer, S., De Wever, B., & Elen, J. (2015). Blended learning in adult education: towards a definition of blended learning (pp. 1–3). Brussels: Genth University.Google Scholar
  6. Bonk, C. (2017). Best practices for online and blended learning: Introducing the R2D2 and TEC-VARIETY models. Paper presented at the Fall Faculty Development Conference, Indianapolis, IN.Google Scholar
  7. Borkowski, J. G., Carr, M., Rellinger, E., & Pressley, M. (1990). Self-regulated cognition: Interdependence of metacognition, attributions, and self-esteem. Dimensions of thinking and cognitive instruction, 1, 53–92.Google Scholar
  8. Boud, D., Keogh, R., & Walker, D. (2013). Reflection: Turning experience into learning. New York: Routledge.Google Scholar
  9. Butler, D. L. (1998). The strategic content learning approach to promoting self-regulated learning: A report of three studies. Journal of Educational Psychology, 90(4), 682–697. Scholar
  10. Carver, C. S., & Scheier, M. (1990). Principles of self-regulation: Action and emotion. New York, NY: Guilford Press.Google Scholar
  11. Cook, D., & Ralston, J. (2003). Sharpening the focus: Methodological issues in analysing on-line conferences. Technology, Pedagogy and Education, 12(3), 361–376.CrossRefGoogle Scholar
  12. Dabbagh, N., & Kitsantas, A. (2004). Supporting self-regulation in student-centered web-based learning environments. International Journal on E-Learning, 3(1), 40–47.Google Scholar
  13. Davis, E. A., & Linn, M. C. (2000). Scaffolding students’ knowledge integration: Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819–837. Scholar
  14. Deci, E. L., & Ryan, R. M. (2010). Self-determination. New Jersey: Wiley Online Library.Google Scholar
  15. Deschacht, N., & Goeman, K. (2015). The effect of blended learning on course persistence and performance of adult learners: A difference-in-differences analysis. Computers & Education, 87, 83–89. Scholar
  16. Devedžić, V. (2006). Semantic web and education, Integrated series in information systems (Vol. 12). Boston: Springer.Google Scholar
  17. Dewey, J. (1958). Experience and nature (Vol. 1). New York: Courier Corporation.Google Scholar
  18. Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. Scholar
  19. Ertmer, P. A., Newby, T. J., & MacDougall, M. (1996). Students’ responses and approaches to case-based instruction: The role of reflective self-regulation. American Educational Research Journal, 33(3), 719–752.CrossRefGoogle Scholar
  20. Farrall, S. (2007). Desistance studies vs. cognitive-behavioural therapies: Which offers most hope for the long term. In Dictionary of probation and offender management (p. 178). Cullompton, UK: Willan Publishing.Google Scholar
  21. Feng, C. Y., & Chen, M. P. (2014). The effects of goal specificity and scaffolding on programming performance and self-regulation in game design. British Journal of Educational Technology, 45(2), 285–302.CrossRefGoogle Scholar
  22. Fleiss, J. (1993). Review papers: The statistical basis of meta-analysis. Statistical Methods in Medical Research, 2(2), 121–145.CrossRefGoogle Scholar
  23. Garza, R. (2009). Latino and white high school Students' perceptions of caring behaviors are we culturally responsive to our students? Urban Education, 44(3), 297–321.CrossRefGoogle Scholar
  24. Graham, C. R., Henrie, C. R., & Gibbons, A. S. (2014). Developing models and theory for blended learning research. Blended learning: Research perspectives, 2, 13–33.Google Scholar
  25. Guerra, J., Hosseini, R., Somyurek, S., & Brusilovsky, P. (2016). An intelligent interface for learning content: Combining an open learner model and social comparison to support self-regulated learning and engagement. Paper presented at the Proceedings of the 21st International Conference on Intelligent User Interfaces, Sonoma, California.Google Scholar
  26. Henri, F. (1992). Computer conferencing and content analysis. In Collaborative learning through computer conferencing (pp. 117–136). Berlin, Germany: Springer.CrossRefGoogle Scholar
  27. Ifenthaler, D. (2012). Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Educational Technology & Society, 15(1), 38–52.Google Scholar
  28. Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M. (2016). How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning and Instruction, 43, 39–51.CrossRefGoogle Scholar
  29. Kassab, S. E., Al-Shafei, A. I., Salem, A. H., & Otoom, S. (2015). Relationships between the quality of blended learning experience, self-regulated learning, and academic achievement of medical students: A path analysis. Advances in Medical Education and Practice, 6, 27–34. Scholar
  30. Kizilcec, R. F., Perez-Sanagustin, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. Scholar
  31. Kuo, Y.-C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50.CrossRefGoogle Scholar
  32. Lallé, S., Taub, M., Mudrick, N. V., Conati, C., & Azevedo, R. (2017). The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor. Paper presented at the International Conference on Artificial Intelligence in Education, Wuhan, China.Google Scholar
  33. Lawless, K. A., & Brown, S. W. (1997). Multimedia learning environments: Issues of learner control and navigation. Instructional Science, 25(2), 117–131. Scholar
  34. Ley, K., & Young, D. B. (2001). Instructional principles for self-regulation. Etr&D – Educational Technology Research and Development, 49(2), 93–103. Scholar
  35. Lin, J. W., Lai, Y. C., & Chang, L. C. (2016). Fostering self-regulated learning in a blended environment using group awareness and peer assistance as external scaffolds. Journal of Computer Assisted Learning, 32(1), 77–93.CrossRefGoogle Scholar
  36. Martinez, M. (2002). Designing learning objects to personalize learning. In D. A. Wiley (Ed.), The instructional use of learning objects (pp. 151–171). Bloomington, Indiana: Association for Educational Communications & Technology.Google Scholar
  37. McCardle, L., & Hadwin, A. F. (2015). Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning. Metacognition and Learning, 10(1), 43–75.CrossRefGoogle Scholar
  38. Merrill, M. D. (2012). First principles of instruction. New York: John Wiley & Sons.Google Scholar
  39. Michalsky, T., & Kramarski, B. (2015). Prompting reflections for integrating self-regulation into teacher technology education. Teachers College Record, 117(5), 1–38.Google Scholar
  40. Moon, J. (1999). Reflection in learning and professional development. Abingdon, UK: Routledge Falmer.Google Scholar
  41. Moos, D. C., & Azevedo, R. (2009). Learning with computer-based learning environments: A literature review of computer self-efficacy. Review of Educational Research, 79(2), 576–600. Scholar
  42. Murray, G. (2014). The social dimensions of learner autonomy and self-regulated learning. Studies in Self-Access Learning Journal, 5(4), 320–341.Google Scholar
  43. Niemiec, R. P., Sikorski, C., & Walberg, H. J. (1996). Learner-control effects: A review of reviews and a meta-analysis. Journal of Educational Computing Research, 15(2), 157–174. Scholar
  44. Nietfeld, J. L., Cao, L., & Osborne, J. W. (2006). The effect of distributed monitoring exercises and feedback on performance, monitoring accuracy, and self-efficacy. Metacognition and Learning, 1(2), 159–179.CrossRefGoogle Scholar
  45. Oliver, M., & Trigwell, K. (2005). Can ‘blended learning’ be redeemed. e-Learning, 2(1), 17–26. Scholar
  46. Oxford, R. L. (2016). Teaching and researching language learning strategies: Self-regulation in context. New York: Taylor & Francis.Google Scholar
  47. Perry, N., & Drummond, L. (2002). Helping young students become self-regulated researchers and writers. Reading Teacher, 56(3), 298–310.Google Scholar
  48. Perry, N. E., Nordby, C. J., & VandeKamp, K. O. (2003). Promoting self-regulated reading and writing at home and school. Elementary School Journal, 103(4), 317–338. Scholar
  49. Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Practice, 41(4), 219–225. Scholar
  50. 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
  51. Puustinen, M., & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45(3), 269–286. Scholar
  52. Reeves, T. C., & Reeves, P. M. (1997). Effective dimensions of interactive learning on the World Wide Web. In Web-based instruction (pp. 59–66). Englewood Cliffs, NJ: Educational Technology.Google Scholar
  53. Reigeluth, C. M. (2013). Instructional-design theories and models: A new paradigm of instructional theory (Vol. 2). New York, NY: Routledge.Google Scholar
  54. Resnick, L. B. (1972). Open education – some tasks for technology. Educational Technology, 12(1), 70–76.Google Scholar
  55. Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological issues in the content analysis of computer conference transcripts. International Journal of Artificial Intelligence in Education (IJAIED), 12, 8–22.Google Scholar
  56. Salomon, G., & Perkins, D. N. (1998). Chapter 1: Individual and social aspects of learning. Review of Research in Education, 23(1), 1–24.CrossRefGoogle Scholar
  57. Schmidt, R. C. (1997). Managing Delphi surveys using nonparametric statistical techniques. Decision Sciences, 28(3), 763–774. Scholar
  58. Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1–2), 111–139. Scholar
  59. Schunk, D. H. (1998). Teaching elementary students to self-regulate practice of mathematical skills with modeling. New York, NY: The Guilford Press.Google Scholar
  60. Shavelson, R. J., Phillips, D. C., Towne, L., & Feuer, M. J. (2003). On the science of education design studies. Educational Researcher, 32(1), 25–28.CrossRefGoogle Scholar
  61. Sims, R., & Hedberg, J. (1995). Dimensions of Learner Control A Reappraisal for Interactive Multimedia Instruction. Paper presented at the Australasian Society for Computers in Learning in Tertiary Education, Melbourne, VIC.Google Scholar
  62. Song, H. S., Kalet, A. L., & Plass, J. L. (2016). Interplay of prior knowledge, self-regulation and motivation in complex multimedia learning environments. Journal of Computer Assisted Learning, 32(1), 31–50.CrossRefGoogle Scholar
  63. Sonnenberg, C., & Bannert, M. (2015). Discovering the effects of metacognitive prompts on the sequential structure of SRL-processes using process mining techniques. Journal of Learning Analytics, 2(2015), 72–100.Google Scholar
  64. Steiner, H. H. (2016). The strategy project: Promoting self-regulated learning through an authentic assignment. International Journal of Teaching and Learning in Higher Education, 28(2), 271–282.Google Scholar
  65. Stevenson, M. P., Hartmeyer, R., & Bentsen, P. (2017). Systematically reviewing the potential of concept mapping technologies to promote self-regulated learning in primary and secondary science education. Educational Research Review, 21, 1–16. Scholar
  66. Strijbos, J.-W., Martens, R. L., Prins, F. J., & Jochems, W. M. (2006). Content analysis: What are they talking about? Computers & Education, 46(1), 29–48.CrossRefGoogle Scholar
  67. Sutton, L. A. (2001). The principle of vicarious interaction in computer-mediated communications. International Journal of Educational Telecommunications, 7(3), 223–242.Google Scholar
  68. Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74. Scholar
  69. Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational Research, 81(1), 4–28. Scholar
  70. Thiede, K. W., Anderson, M. C. M., & Therriault, D. (2003). Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95(1), 66–73. Scholar
  71. Türker, M. A., & Zingel, S. (2008). Formative interfaces for scaffolding self-regulated learning in PLEs. elearning Papers, 14(9, July).Google Scholar
  72. Van Laer, S., & Elen, J. (2016). In search of attributes that support self-regulation in blended learning environments. Education and Information Technologies, 22(4), 1395–1454. Scholar
  73. van Merriënboer, J. J., & Kirschner, P. A. (2017). Ten steps to complex learning: A systematic approach to four-component instructional design. New York: Routledge.Google Scholar
  74. Veenman, M. V. J., Elshout, J. J., & Meijer, J. (1997). The generality vs domain-specificity of metacognitive skills in novice learning across domains. Learning and Instruction, 7(2), 187–209. Scholar
  75. Verpoorten, D., Westera, W., & Specht, M. (2017). Effects of isolated versus combined learning enactments in an online course. International Journal of Technology Enhanced Learning, 9(2–3), 169–185. Scholar
  76. Vygotsky, L. (1978). Interaction between learning and development. Readings on the development of children, 23(3), 34–41.Google Scholar
  77. Wiggins, G. P. (1993). Assessing student performance: Exploring the purpose and limits of testing. San Francisco, CA: Jossey-Bass.Google Scholar
  78. Williams, M. D. (1993). A Comprehensive Review of Learner-Control: The Role of Learner Characteristics. Paper presented at the Association for Educational Communications and Technology, New Orleans, LA.Google Scholar
  79. Wilson, S., Liber, O., Johnson, M., Beauvoir, P., Sharples, P., & Milligan, C. (2007). Personal learning environments: Challenging the dominant design of educational systems. Journal of E-Learning and Knowledge Society, 3(2), 27–38.Google Scholar
  80. Winne, P. H., & Hadwin, A. (2013). nStudy: Tracing and supporting self-regulated learning in the internet. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (Vol. 28, pp. 293–308). New York, NY: Springer.CrossRefGoogle Scholar
  81. Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and Higher Education, 10(1), 15–25.CrossRefGoogle Scholar
  82. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100.CrossRefGoogle Scholar
  83. Zimmerman, B. J., & Schunk, D. (2006). Competence and control beliefs: Distinguishing the means and ends. In Handbook of educational psychology (pp. 349–367). Mahwah, NJ: Erlbaum.Google Scholar

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Authors and Affiliations

  1. 1.Centre for Instructional Psychology and TechnologyKU LeuvenLeuvenBelgium

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