Effects of case library recommendation system on problem solving and knowledge structure development

Abstract

Case-based reasoning posits that learners can use their prior experience to solve new problems. This theory is cited to explain the benefits of problem-based learning (PBL), especially as it relates to knowledge structure development. However, critics argue that learners lack the relevant knowledge structures to simultaneously learn new content and solve complex problems. In terms of learning design, theorists suggest a set of cases (case library) can be used as vicarious memory and thus bridge the experience gap. While this may be beneficial in theory, studies show experts and novices tend to process the details of a case in markedly different ways, which would be problematic in terms of case libraries' ability to scaffolding problem-solving. To address this challenge, this study compared the following conditions in terms of argumentation and knowledge structure development: PBL only, PBL with static case library, PBL with recommendation system case library. Both the case library conditions outperformed the PBL-only condition in terms of initial argument development. However, the PBL with recommendation system case library outperformed the other conditions on rebuttal development. Implications for PBL, CBR, knowledge structure development, and learning design are discussed.

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References

  1. Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (2013). Learning through case comparisons: A meta-analytic review. Educational Psychologist,48(2), 87–113.

    Article  Google Scholar 

  2. Asterhan, C. S. C., & Dotan, A. (2018). Feedback that corrects and contrasts students’ erroneous solutions with expert ones improves expository instruction for conceptual change. Instructional Science,46(3), 337–355.

    Article  Google Scholar 

  3. Ausubel, D. G. (1963). Cognitive structure and the facilitation of meaningful verbal learning. Journal of Teacher Education,14(2), 217–222.

    Article  Google Scholar 

  4. Balabanović, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM,40(3), 66–72.

    Article  Google Scholar 

  5. Balslev, T., De Grave, W., Muijtjens, A., & Scherpbier, A. (2005). Comparison of text and video cases in a postgraduate problem-based learning format. Medical Education,39(11), 1086–1092.

    Article  Google Scholar 

  6. Barrows, H., & Tamblyn, R. (1980). Problem-based learning: An approach to medical education. New York, NY: Springer.

    Google Scholar 

  7. Bédard, D., Lison, C., Dalle, D., Côté, D., & Boutin, N. (2012). Problem-based and project-based learning in engineering and medicine: Determinants of students’ engagement and persistance. Interdisciplinary Journal of Problem-Based Learning,6(2), 7. https://doi.org/10.7771/1541-5015.1355.

    Article  Google Scholar 

  8. Belland, B., Walker, A., Kim, N., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education. Review of Educational Research,87(2), 309–344.

    Article  Google Scholar 

  9. Bennett, S. (2010). Investigating strategies for using related cases to support design problem solving. Educational Technology Research and Development,58(4), 459–480.

    Article  Google Scholar 

  10. Boshuizen, H. P. A., Wiel, M. W. J., & Schmidt, H. G. (2012). What and how advanced medical students learn from reasoning through multiple cases. Instructional Science,40(5), 755–768.

    Article  Google Scholar 

  11. Cattaneo, A. A. P., & Boldrini, E. (2017). Learning from errors in dual vocational education: Video-enhanced instructional strategies. Journal of Workplace Learning,29(5), 357–373.

    Article  Google Scholar 

  12. Clariana, R. B., Wolfe, M. B., & Kim, K. (2014). The influence of narrative and expository lesson text structures on knowledge structures: Alternate measures of knowledge structure. Educational Technology Research and Development,62(5), 601–616.

    Article  Google Scholar 

  13. Crowell, A., & Kuhn, D. (2014). Developing dialogic argumentation skills: A 3-year intervention study. Journal of Cognition and Development,15(2), 363–381.

    Article  Google Scholar 

  14. Engle, R. A., Nguyen, P. D., & Mendelson, A. (2011). The influence of framing on transfer: Initial evidence from a tutoring experiment. Instructional Science,39(5), 603–628.

    Article  Google Scholar 

  15. Ertmer, P. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development,53(4), 25–39.

    Article  Google Scholar 

  16. Ertmer, P., & Koehler, A. A. (2018). Facilitation strategies and problem space coverage: Comparing face-to-face and online case-based discussions. Educational Technology Research and Development,66(3), 639–670.

    Article  Google Scholar 

  17. Ertmer, P., Stepich, D. A., York, C. S., Stickman, A., Wu, X. L., Zurek, S., et al. (2008). How instructional design experts use knowledge and experience to solve ill-structured problems. Performance Improvement Quarterly,21(1), 17–42.

    Article  Google Scholar 

  18. Ertmer, P., Glazewski, K. D., Jones, D., Ottenbreit-Leftwich, A., Goktas, Y., Collins, K., et al. (2009). Facilitating technology-enhanced problem-based learning (PBL) in the middle school classroom: An examination of how and why teachers adapt. Journal of Interactive Learning Research,20(1), 35–54.

    Google Scholar 

  19. Eseryel, D., Ifenthaler, D., & Ge, X. (2013). Validation study of a method for assessing complex ill-structured problem solving by using causal representations. Educational Technology, Research and Development,61(3), 443–463.

    Article  Google Scholar 

  20. Exarchos, T. P., Papaloukas, C., Lampros, C., & Fotiadis, D. I. (2008). Mining sequential patterns for protein fold recognition. Journal of Biomedical Informatics,41(1), 165–179.

    Article  Google Scholar 

  21. Farrelly, C. M., Schwartz, S. J., Lisa Amodeo, A., Feaster, D. J., Steinley, D. L., Meca, A., et al. (2017). The analysis of bridging constructs with hierarchical clustering methods: An application to identity. Journal of Research in Personality,70, 93–106.

    Article  Google Scholar 

  22. Fitzgerald, G., Mitchem, K., Hollingsead, C., Miller, K., Koury, K., & Tsai, H.-H. (2011). Exploring the bridge from multimedia cases to classrooms: Evidence of transfer. Journal of Special Education Technology,26(2), 23–38.

    Article  Google Scholar 

  23. Forina, M., Armanino, C., & Raggio, V. (2002). Clustering with dendrograms on interpretation variables. Analytica Chimica Acta,454(1), 13–19.

    Article  Google Scholar 

  24. Gartmeier, M., Bauer, J., Fischer, M. R., Hoppe-Seyler, T., Karsten, G., Kiessling, C., et al. (2015). Fostering professional communication skills of future physicians and teachers: Effects of e-learning with video cases and role-play. Instructional Science,43(4), 443–462.

    Article  Google Scholar 

  25. Graesser, A. C., & Olde, B. A. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology,95(3), 524–536.

    Article  Google Scholar 

  26. Hartling, L., Spooner, C., Tjosvold, L., & Oswald, A. (2010). Problem-based learning in pre-clinical medical education: 22 years of outcome research. Medical Teacher,32(1), 28–35.

    Article  Google Scholar 

  27. Hemberger, L., Kuhn, D., Matos, F., & Shi, Y. (2017). A dialogic path to evidence-based argumentive writing. Journal of the Learning Sciences,26(4), 575–607.

    Article  Google Scholar 

  28. Hernandez-Serrano, J., & Jonassen, D. H. (2003). The effects of case libraries on problem solving. Journal of Computer Assisted Learning,19(1), 103–114.

    Article  Google Scholar 

  29. Herr, N. (2008). The sourcebook for teaching science, grades 6–12: Strategies, activities, and instructional resources. San Francisco, CA: Wiley.

    Google Scholar 

  30. Hmelo-Silver, C. (2013). Creating a learning space in problem-based learning. Interdisciplinary Journal of Problem-Based Learning,7(1), 24–39. https://doi.org/10.7771/1541-5015.1334.

    Article  Google Scholar 

  31. Hmelo-Silver, C., & Barrows, H. (2006). Goals and strategies of a problem-based learning facilitator. Interdisciplinary Journal of Problem-Based Learning,1(1), 21–39. https://doi.org/10.7771/1541-5015.1004.

    Article  Google Scholar 

  32. Hmelo-Silver, C., Duncan, R. G., & Chinn, C. (2007a). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist,42(2), 99–107.

    Article  Google Scholar 

  33. Hmelo-Silver, C., Marathe, S., & Liu, L. (2007b). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences,16(3), 307–331.

    Article  Google Scholar 

  34. Hung, W. (2011). Theory to reality: A few issues in implementing problem-based learning. Educational Technology Research and Development,59(4), 529–552.

    Article  Google Scholar 

  35. Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity,6(3), 41–49.

    Article  Google Scholar 

  36. Jacobson, M. J., & Spiro, R. (1995). Hypertext learning environments, cognitive flexibility, and the transfer of complex knowledge: An empirical investigation. Journal of Educational Computing Research,12(4), 301–333.

    Article  Google Scholar 

  37. Jeong, H., & Hmelo-Silver, C. E. (2016). Seven affordances of computer-supported collaborative learning: How to support collaborative learning? How can technologies help? Educational Psychologist,51(2), 247–265.

    Article  Google Scholar 

  38. Jokhan, A., Sharma, B., & Singh, S. (2018). Early warning system as a predictor for student performance in higher education blended courses. Studies in Higher Education,44, 1900–1911.

    Article  Google Scholar 

  39. Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development,45(1), 65–94.

    Article  Google Scholar 

  40. Jonassen, D. H. (2011). ASK Systems: Interrogative access to multiple ways of thinking. Educational Technology Research and Development,59(1), 159–175.

    Article  Google Scholar 

  41. Jonassen, D. H., & Cho, Y. (2011). Fostering argumentation while solving engineering ethics problems. Journal of Engineering Education,100(4), 680–702.

    Article  Google Scholar 

  42. Jonassen, D. H., & Hernandez-Serrano, J. (2002). Case-based reasoning and instructional design: Using stories to support problem solving. Educational Technology Research and Development,50(2), 65–77.

    Article  Google Scholar 

  43. Jonassen, D. H., & Hung, W. (2008). All problems are not equal: Implications for problem-based learning. Interdisciplinary Journal of Problem-Based Learning,2(2), 6–28.

    Article  Google Scholar 

  44. Jonassen, D. H., Beissner, K., & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. New York: Psychology Press.

    Google Scholar 

  45. Ju, H., & Choi, I. (2017). The role of argumentation in hypothetico-deductive reasoning during problem-based learning in medical education: A conceptual framework. Interdisciplinary Journal of Problem-Based Learning,12(1), 4. https://doi.org/10.7771/1541-5015.1638.

    Article  Google Scholar 

  46. Kim, K. (2017). Visualizing first and second language interactions in science reading: A knowledge structure network approach. Language Assessment Quarterly,14(4), 328–345.

    Article  Google Scholar 

  47. Kim, K., & Clariana, R. B. (2015). Knowledge structure measures of reader’s situation models across languages: Translation engenders richer structure. Technology, Knowledge and Learning,20(2), 249–268.

    Article  Google Scholar 

  48. Kim, H., & Hannafin, M. J. (2011). Developing situated knowledge about teaching with technology via web-enhanced case-based activity. Computers & Education,57(1), 1378–1388.

    Article  Google Scholar 

  49. Kim, J., Jo, I.-H., & Park, Y. (2016). Effects of learning analytics dashboard: Analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review,17(1), 13–24.

    Article  Google Scholar 

  50. Kim, N. J., Belland, B., & Walker, A. E. (2017). Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis. Educational Psychology Review,30(2), 397–429.

    Article  Google Scholar 

  51. Kirschner, P., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist,48(3), 169–183.

    Article  Google Scholar 

  52. Kirschner, P., Sweller, J., & Clark, R. (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(2), 75–86.

    Article  Google Scholar 

  53. Kolodner, J. (1991). Improving human decision making through case-based decision aiding. AI Magazine,12(2), 52–68.

    Google Scholar 

  54. Kolodner, J. (1997). Educational implications of analogy: A view from case-based reasoning. The American Psychologist,52(1), 57–66.

    Article  Google Scholar 

  55. Kolodner, J., Owensby, J., & Guzdial, M. (2004). Case-based learning aids. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology: A project of the Association for Educational Communications and Technology (2nd ed., pp. 829–861). Mahwah, NJ: LEA.

    Google Scholar 

  56. Kuhn, D. (1993). Science as argument: Implications for teaching and learning scientific thinking. Science Education,77(3), 319–337.

    Article  Google Scholar 

  57. Kuiper, F. K., & Fisher, L. (1975). 391: A Monte Carlo comparison of six clustering procedures. Biometrics,31(3), 777–783.

    Article  Google Scholar 

  58. Lachner, A., Jarodzka, H., & Nückles, M. (2016). What makes an expert teacher? Investigating teachers’ professional vision and discourse abilities. Instructional Science,44(3), 197–203.

    Article  Google Scholar 

  59. Lajoie, S. P., Hmelo-Silver, C. E., Wiseman, J. G., Chan, L. K., Lu, J., Khurana, C., et al. (2014). Using online digital tools and video to support international problem-based learning. Interdisciplinary Journal of Problem-Based Learning,8(2), 60–75. https://doi.org/10.7771/1541-5015.1412.

    Article  Google Scholar 

  60. Langfelder, P., Zhang, B., & Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: The Dynamic Tree Cut package for R. Bioinformatics,24(5), 719–720.

    Article  Google Scholar 

  61. Lazonder, A., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research,87(4), 1–38.

    Google Scholar 

  62. Lin-Siegler, X., Shaenfield, D., & Elder, A. D. (2015). Contrasting case instruction can improve self-assessment of writing. Educational Technology Research and Development,63(4), 517–537.

    Article  Google Scholar 

  63. Lin-Siegler, X., Ahn, J. N., Chen, J., Fang, F.-F. A., & Luna-Lucero, M. (2016). Even Einstein struggled: Effects of learning about great scientists’ struggles on high school students’ motivation to learn science. Journal of Educational Psychology,108(3), 314.

    Article  Google Scholar 

  64. Loyens, S., Jones, S. H., Mikkers, J., & van Gog, T. (2015). Problem-based learning as a facilitator of conceptual change. Learning and Instruction,38, 34–42.

    Article  Google Scholar 

  65. Luo, H., Koszalka, T. A., Arnone, M. P., & Choi, I. (2018). Applying case-based method in designing self-directed online instruction: A formative research study. Educational Technology Research and Development,66(2), 515–544.

    Article  Google Scholar 

  66. Musen, M. A., Middleton, B., & Greenes, R. A. (2014). Clinical decision-support systems. In E. H. Shortliffe & J. J. Cimino (Eds.), Biomedical Informatics (pp. 643–674). London: Springer.

    Chapter  Google Scholar 

  67. Newell, G. E., Beach, R., Smith, J., & VanDerHeide, J. (2011). Teaching and learning argumentative reading and writing: A review of research. Reading Research Quarterly,46(3), 273–304.

    Google Scholar 

  68. Nussbaum, E. M., & Schraw, G. (2007). Promoting argument-counterargument integration in students’ writing. Journal of Experimental Education,76(1), 59–92.

    Article  Google Scholar 

  69. Reiser, B. (2004). Scaffolding complex learning: the mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304.

    Article  Google Scholar 

  70. Reynolds, A. P., Richards, G., de la Iglesia, B., & Rayward-Smith, V. J. (2006). Clustering rules: A comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms,5(4), 475–504.

    Article  Google Scholar 

  71. Riesbeck, C. K., & Schank, R. C. (2013). Inside case-based reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates Publishers.

    Book  Google Scholar 

  72. Rong, H., & Choi, I. (2018). Integrating failure in case-based learning: A conceptual framework for failure classification and its instructional implications. Educational Technology Research and Development. https://doi.org/10.1007/s11423-018-9629-3.

    Article  Google Scholar 

  73. Salinitri, F. D., Wilhelm, S. M., & Crabtree, B. L. (2015). Facilitating facilitators: Enhancing PBL through a structured facilitator development program. Interdisciplinary Journal of Problem-Based Learning,9(1), 73–82. https://doi.org/10.7771/1541-5015.1509.

    Article  Google Scholar 

  74. Schafer, B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 291–324). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  75. Schank, R. (1999). Dynamic memory revisited (2nd ed.). Cambridge, England: Cambridge University Press.

    Book  Google Scholar 

  76. Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 253–260). New York: ACM.

  77. Schenke, K., & Richland, L. E. (2017). Preservice teachers’ use of contrasting cases in mathematics instruction. Instructional Science,45(3), 311–329.

    Article  Google Scholar 

  78. Snapir, Z., Eberbach, C., Ben-Zvi-Assaraf, O., Hmelo-Silver, C., & Tripto, J. (2017). Characterising the development of the understanding of human body systems in high-school biology students—A longitudinal study. International Journal of Science Education,39(15), 2092–2127.

    Article  Google Scholar 

  79. Tang, H., & Clariana, R. (2017). Leveraging a sorting task as a measure of knowledge structure in bilingual settings. Technology, Knowledge and Learning,22(1), 23–35.

    Article  Google Scholar 

  80. Tawfik, A. A. (2017). Do cases teach themselves? A comparison of case library prompts in supporting problem-solving during argumentation. Journal of Computing in Higher Education,29(2), 267–285.

    Article  Google Scholar 

  81. Tawfik, A. A., & Jonassen, D. H. (2013). The effects of successful versus failure-based cases on argumentation while solving decision-making problems. Educational Technology Research and Development,61(3), 385–406.

    Article  Google Scholar 

  82. Tawfik, A. A., & Kolodner, J. (2016). Systematizing scaffolding for problem-based learning: A view from case-based reasoning. Interdisciplinary Journal of Problem-Based Learning,10(1), 6.

    Article  Google Scholar 

  83. Tawfik, A. A., Jonassen, D. H., & Keene, C. W. (2012). Why do we fall? Using experiences of failure to design case libraries. International Journal of Designs for Learning,3(1), 1–11.

    Article  Google Scholar 

  84. Tawfik, A. A., Gill, A., Hogan, M., York, C. S., & Keene, C. W. (2019). How novices use expert case libraries for problem solving. Technology, Knowledge and Learning,24(1), 23–40.

    Article  Google Scholar 

  85. Valentine, K. D., & Kopcha, T. J. (2016). The embodiment of cases as alternative perspective in a mathematics hypermedia learning environment. Educational Technology Research and Development,64(6), 1183–1206.

    Article  Google Scholar 

  86. Walker, A., & Leary, H. (2009). A problem based learning Meta analysis: Differences across problem types, implementation types, disciplines, and assessment levels. Interdisciplinary Journal of Problem-Based Learning,3(1), 12–43.

    Article  Google Scholar 

  87. Wang, M., Wu, B., Kinshuk, Chen, N.-S., & Spector, J. M. (2013). Connecting problem-solving and ***knowledge-construction processes in a visualization-based learning environment. Computers & Education,68, 293–306.

    Article  Google Scholar 

  88. Wijnen, M., Loyens, S., Smeets, G., Kroeze, M., & Van der Mollen, H. (2017). Students’ and teachers’ experiences with the implementation of problem-based learning at a university law school. Interdisciplinary Journal of Problem-Based Learning,11(2), 5. https://doi.org/10.7771/1541-5015.1681.

    Article  Google Scholar 

  89. Wolff, C. E., Jarodzka, H., van den Bogert, N., & Boshuizen, H. P. A. (2016). Teacher vision: Expert and novice teachers’ perception of problematic classroom management scenes. Instructional Science,44(3), 243–265.

    Article  Google Scholar 

  90. Woods, D. R., Hrymak, A. N., Marshall, R. R., Wood, P. E., Crowe, C. M., Hoffman, T. W., et al. (1997). Developing problem solving skills: The McMaster problem solving program. Journal of Engineering Education,86(2), 75–91.

    Article  Google Scholar 

  91. Xiong, N. (2011). Learning fuzzy rules for similarity assessment in case-based reasoning. Expert Systems with Applications,38(9), 10780–10786.

    Article  Google Scholar 

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The authors would like to thank Jon Davison for his helpful comments and feedback during the review process.

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Tawfik, A.A., Kim, K. & Kim, D. Effects of case library recommendation system on problem solving and knowledge structure development. Education Tech Research Dev 68, 1329–1353 (2020). https://doi.org/10.1007/s11423-020-09737-w

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Keywords

  • Case-based reasoning
  • Case libraries
  • Contrasting cases
  • Problem-based learning
  • Inquiry-based learning
  • Recommendation systems