Technology, Knowledge and Learning

, Volume 23, Issue 1, pp 177–187 | Cite as

Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval

  • Andrew A. TawfikEmail author
  • Hamed Alhoori
  • Charles Wayne Keene
  • Christian Bailey
  • Maureen Hogan
Original research


In case library learning environments, learners are presented with an array of narratives that can be used to guide their problem solving. However, according to theorists, learners struggle to identify and retrieve the optimal case to solve a new problem. Given the challenges novice face during case retrieval, recommender systems can be embedded in case libraries to support the decision-making process about which case is most relevant to solve new problems. This emerging technology reports how experts’ assessment of case relevancy was used to retrieve and suggest the most relevant cases for the learner as they engaged in an inquiry-based learning. Specifically, our case library learning system integrates a content-based filtering, which recommends items similar to those a user has selected based on item descriptions or other user data, and is most widely used in textual domains. Implications for practice are also discussed.


Recommender systems Case-based reasoning Problem solving Case retrieval 


  1. Amatriain, X., Lathia, N., Pujol, J. M., Kwak, H., & Oliver, N. (2009). The wisdom of the few: A collaborative filtering approach based on expert opinions from the web. In Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval (pp. 532–539). New York, NY, USA: ACM.Google Scholar
  2. Balabanović, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66–72.CrossRefGoogle Scholar
  3. Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2016). Synthesizing results from empirical research on computer-based scaffolding in STEM education. Review of Educational Research. doi: 10.3102/0034654316670999.Google Scholar
  4. Bousquet, O., & Bottou, L. (2008). The tradeoffs of large scale learning. In J. C. Platt, D. Koller, Y. Singer, & S. T. Roweis (Eds.), Advances in neural information processing systems 20 (pp. 161–168). Red Hook: Curran Associates, Inc.Google Scholar
  5. Danish, J. A. (2014). Applying an activity theory lens to designing instruction for learning about the structure, behavior, and function of a honeybee system. Journal of the Learning Sciences, 23(2), 100–148.CrossRefGoogle Scholar
  6. Ertmer, P., & Koehler, A. A. (2014). Online case-based discussions: Examining coverage of the afforded problem space. Educational Technology Research and Development, 62(5), 617–636.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70.CrossRefGoogle Scholar
  10. Hernandez-Serrano, J., & Jonassen, D. H. (2003). The effects of case libraries on problem solving. Journal of Computer Assisted learning, 19(1), 103–114.CrossRefGoogle Scholar
  11. Herrington, J., Reeves, T. C., & Oliver, R. (2014). Authentic learning environments. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 453–464). New York, NY: Springer.Google Scholar
  12. Hill, W., Stead, L., Rosenstein, M., & Furnas, G. (1995). Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 194–201). New York, NY, USA: ACM Press/Addison-Wesley Publishing Co.Google Scholar
  13. Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. Journal of the Learning Sciences, 16(3), 307–331.CrossRefGoogle Scholar
  14. Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28(1), 127–138.CrossRefGoogle Scholar
  15. Ifenthaler, D. (2017). Learning analytics design. In L. Lin & M. Spector (Eds.), The sciences of learning and instructional design: Constructive articulation between communities (pp. 202–211). New York, NY: Routledge.Google Scholar
  16. Ifenthaler, D., Masduki, I., & Seel, N. M. (2011). The mystery of cognitive structure and how we can detect it: Tracking the development of cognitive structures over time. Instructional Science, 39(1), 41–61.CrossRefGoogle Scholar
  17. Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.CrossRefGoogle Scholar
  18. Jeong, H., & Hmelo-Silver, C. E. (2010). Productive use of learning resources in an online problem-based learning environment. Computers in Human Behavior, 26(1), 84–99.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Jonassen, D. H. (2011). ASK systems: Interrogative access to multiple ways of thinking. Educational Technology Research and Development, 59(1), 159–175.CrossRefGoogle Scholar
  22. Jonassen, D. H., & Hung, W. (2008). All problems are not equal: implications for problem-based learning. Interdisciplinary Journal of Problem-Based Learning, 2(2). Retrieved from
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. Kolodner, J. (1991). Improving human decision making through case-based decision aiding. AI Magazine, 12(2), 52–68.Google Scholar
  26. Kolodner, J. L., Owensby, J. N., & 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: Lawrence Erlbaum Associates.Google Scholar
  27. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), 77–87.CrossRefGoogle Scholar
  28. Koren, Y., Bell, R., Volinsky, C., et al. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.CrossRefGoogle Scholar
  29. 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), 6.CrossRefGoogle Scholar
  30. Lazonder, A., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 87(4), 1–38.Google Scholar
  31. Leary, H., & Walker, A. (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). Retrieved from
  32. Loh, C. S., Sheng, Y., & Li, I.-H. (2015). Predicting expert–novice performance as serious games analytics with objective-oriented and navigational action sequences. Computers in Human Behavior, 49, 147–155.CrossRefGoogle Scholar
  33. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901.CrossRefGoogle Scholar
  34. Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 325–341). Berlin: Springer.CrossRefGoogle Scholar
  35. Reiser, B. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304.CrossRefGoogle Scholar
  36. Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.CrossRefGoogle Scholar
  37. 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: Springer.CrossRefGoogle Scholar
  38. Schank, R. (1999). Dynamic memory revisited (2nd ed.). Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
  39. 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, NY, USA: ACM.Google Scholar
  40. Schenke, K., & Richland, L. E. (2017). Preservice teachers’ use of contrasting cases in mathematics instruction. Instructional Science, 45(3), 311–329.CrossRefGoogle Scholar
  41. Schon, D. A. (1984). The reflective practitioner: How professionals think in action (1st ed.). New York: Basic Books.Google Scholar
  42. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating “word of mouth.” In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 210–217). New York: ACM Press/Addison-Wesley Publishing Co.Google Scholar
  43. Shokouhi, S. V., Skalle, P., & Aamodt, A. (2014). An overview of case-based reasoning applications in drilling engineering. Artificial Intelligence Review, 41(3), 317–329.CrossRefGoogle Scholar
  44. Speier, C., Valacich, J. S., & Vessey, I. (1999). The influence of task interruption on individual decision making: an information overload perspective. Decision Sciences, 30(2), 337–360.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 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 & Development, 61(3), 385–406.Google Scholar
  47. Tawfik, A. A., & Kolodner, J. L. (2016). Systematizing scaffolding for problem-based learning: A view from case-based reasoning. Interdisciplinary Journal of Problem-Based Learning, 10(1), 6.Google Scholar
  48. van Merriënboer, J. J. G. (2013). Perspectives on problem solving and instruction. Computers & Education, 64, 153–160.CrossRefGoogle Scholar
  49. Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.Google Scholar
  50. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95.CrossRefGoogle Scholar
  51. Woodruff, A., Gossweiler, R., Pitkow, J., Chi, E. H., & Card, S. K. (2000). Enhancing a digital book with a reading recommender. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 153–160). New York, NY, USA: ACM.Google Scholar
  52. Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Andrew A. Tawfik
    • 1
    Email author
  • Hamed Alhoori
    • 2
  • Charles Wayne Keene
    • 3
  • Christian Bailey
    • 2
  • Maureen Hogan
    • 2
  1. 1.University of MemphisMemphisUSA
  2. 2.Northern Illinois UniversityDekalbUSA
  3. 3.University of MissouriColumbiaUSA

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