Procedural problem solving is an important skill in most technical domains, like programming, but many students reach problem solving impasses and flounder. In most formal learning environments, instructors help students to overcome problem solving impasses by scaffolding initial problem solving. Relying on this type of personalized interaction, however, limits the scale of formal instruction in technical domains, or it limits the efficacy of learning environments without it, like many scalable online learning environments. The present experimental study explored whether learners’ self-explanations of worked examples could be used to provide personalized but non-adaptive scaffolding during initial problem solving to improve later performance. Participants who received their own self-explanations as scaffolding for practice problems performed better on a later problem-solving test than participants who did not receive scaffolding or who received expert’s explanations as scaffolding. These instructional materials were not adaptive, making them easy to distribute at scale, but the use of the learner’s own explanations as scaffolding made them effective.
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Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26, 147–179.
Atkins, S. M., Sprenger, A. M., Colflesh, G. J. H., Briner, T. L., Buchanan, J. B., Chavis, S. E., Chen, S. Y., Iannuzzi, G. L., Kashtelyan, V., Dowling, E., Harbison, J. I., & Dougherty, M. R. (2014). Measuring working memory is all fun and games: A four-dimensional spatial game predicts cognitive task performance. Experimental Psychologist, 61(6), 417–438.
Atkinson, R. K., Catrambone, R., & Merrill, M. M. (2003). Aiding transfer in statistics: Examining the use of conceptually oriented equations and elaborations during subgoal learning. Journal of Educational Psychology, 95(4), 762–773.
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of the Educational Research, 70(2), 181–214. https://doi.org/10.2307/1170661.
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., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008). Why is externally-facilitated regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research and Development, 56(1), 45–72.
Catrambone, R. (1995). Aiding subgoal learning: Effects on transfer. Journal of Educational Psychology, 87(1), 5–17. https://doi.org/10.1037/0022-0622.214.171.124.
Catrambone, R. (1996). Generalizing solution procedures learned from examples. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1020–1031. https://doi.org/10.1037/0278-73126.96.36.1990.
Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127, 355–376. https://doi.org/10.1037/0096-34188.8.131.525.
Catrambone, R. (2011, June). Task analysis by problem solving (TAPS): Uncovering expert knowledge to develop highquality instructional materials and training. In 2011 Learning and technology symposium (Vol. 47, pp. 132–139).
Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105.
Conati, C., & VanLehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11, 389–415.
Czerniewicz, L., Deacon, A., & Walji, S. (2018). Educators, copyright and open education resources in Massive Open Online Courses. In Bajić M, Dohn NB, de Laat M, Jandrić P, Ryberg T (Eds.) Proceedings of the 11th international conference on networked learning (pp. 264–271).
Downes, S. (2013). The role of open educational resources in personal learning. International seminar of the UNESCO chair in e-learning. Universitat Oberta de Catalunya.
Delen, E., Liew, J., & Willson, V. (2014). Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers & Education, 78, 312–320.
García Espinosa, B. J., Tenorio Sepúlveda, G. C., & Ramírez Montoya, M. S. (2015). Self-motivation challenges for student involvement in the Open Educational Movement with MOOC. International Journal of Educational Technology in Higher Education, 12, 91–103. https://doi.org/10.7238/rusc.v12i1.2185.
Grover, S., & Pea, R. (2013). Computational thinking in K–12 a review of the state of the field. Educational Researcher, 42(1), 38–43.
Hill, J. R., & Hannafin, M. J. (2001). Teaching and learning in digital environments: The resurgence of resource-based learning. Educational Technology Research and Development, 49(3), 37–52.
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. Educational Psychologist, 42(2), 99–107.
Hundhausen, C. D., Farley, S. F., & Brown, J. L. (2009). Can direct manipulation lower the barriers to computer programming and promote transfer of training?: An experimental study. ACM Transactions in CHI, 16(3), 1–40. https://doi.org/10.1145/1592440.1592442.
Kim, M. C., & Hannafin, M. J. (2011). Scaffolding problem solving in technology-enhanced learning environments (TELEs): Bridging research and theory with practice. Computers & Education, 56(2), 403–417.
Kizilcec, R. F., Pérez-Sanagustín, 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.
Littlejohn, A., Falconer, I., & Mcgill, L. (2008). Characterising effective eLearning resources. Computers & Education, 50(3), 757–771.
Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by choice: Urban youth learning programming with scratch (Vol. 40, No. 1, pp. 367–371). ACM.
Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in procedural instructions and worked examples. Learning and Instruction, 42, 58–71. https://doi.org/10.1016/j.learninstruc.2015.12.002.
Margulieux, L. E., & Catrambone, R. (2019). Finding the best types of guidance for constructing self-explanations of subgoals in programming. Journal of the Learning Sciences. https://doi.org/10.1080/10508406.2018.1491852
Margulieux, L. E., Catrambone, R., & Guzdial, M. (2016). Employing subgoals in computer programming education. Computer Science Education, 26(1), 44–67. https://doi.org/10.1080/08993408.2016.1144429.
Morrison, B. B., Decker, A., & Margulieux, L. E. (2016). Learning loops: A replication study illuminates impact of HS courses. In Proceedings of the 12th annual international conference on international computing education research (pp. 221–230). Association for Computing Machinery. https://doi.org/10.1145/2960310.2960330
Morrison, B. B., Dorn, B., & Guzdial, M. (2014). Measuring cognitive load in introductory CS: Adaptation of an instrument. In Proceedings of the 10th annual conference on international computing education research (pp. 131–138).
Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015). Subgoals, context, and worked examples in learning computing problem solving. In Proceedings of the 11th annual international conference on international computing education research (pp. 21–29). Association for Computing Machinery. https://doi.org/10.1145/2787622.2787733.
Ossiannilsson, E., Williams, K., Camilleri, A. F., & Brown, M. (2015). Quality models in online and open education around the globe: State of the art and recommendations. International Council for Open and Distance Education (ICDE).
Palmiter, S., Elkerton, J., & Baggett, P. (1991). Animated demonstrations versus written instructions for learning procedural tasks: A preliminary investigation. International Journal of Man-Machine Studies, 34, 687–701. https://doi.org/10.1016/0020-7373(91)90019-4.
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(3), 423–451. https://doi.org/10.1207/s15327809jls1303_6.
Pirolli, P., & Recker, M. (1994). Learning strategies and transfer in the domain of programming. Cognition and Instruction, 12(3), 235–275.
Rohs, M., & Ganz, M. (2015). MOOCs and the claim of education for all: A disillusion by empirical data. The International Review of Research in Open and Distributed Learning, 16(6), 1–19.
Rountree, N., Rountree, J., Robins, A., & Hannah, R. (2004). Interacting factors that predict success and failure in a CSI course. SIGCSE Bulletin, 33(4), 101–104.
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.
Schmidt, H. G., Loyens, S. M., Van Gog, T., & Paas, F. (2007). Problem-based learning is compatible with human cognitive architecture: Commentary on Kirschner, Sweller, and Clark. Educational Psychologist, 42(2), 91–97.
Smyth, R., Bossu, C., & Stagg, A. (2016). Toward an open empowered learning model of pedagogy in higher education. In Open learning and formal credentialing in higher education: curriculum models and institutional policies (pp. 205–222). AEMAL. IGI Publishing (IGI Global).
Soloway, E. (1986). Learning to program = learning to construct mechanisms and explanations. Communications of the ACM, 29(9), 850–858.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138.
Tobias, S., & Duffy, T. M. (Eds.). (2009). Constructivist instruction: Success or failure?. Routledge.
Tulving, E. (1962). Subjective organization in free recall of “unrelated” words. Psychological Review, 69(4), 344–354.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
Vygotsky, L. (1978). Mind in society. . Harvard University Press.
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.
Wylie, R., & Chi, M. T. H. (2014). The self-explanation principle in multimedia learning. In R. Mayer (Ed.) The Cambridge handbook of multimedia learning (2nd ed., pp.413–432). Cambridge University Press.
Yelland, N., & Masters, J. (2007). Rethinking scaffolding in the information age. Computers & Education, 48(3), 362–382.
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Margulieux, L.E., Catrambone, R. Scaffolding problem solving with learners’ own self explanations of subgoals. J Comput High Educ (2021). https://doi.org/10.1007/s12528-021-09275-1
- Problem solving
- Distance education and telelearning
- Post-secondary education
- Programming and programming languages
- Teaching/learning strategies