Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval
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.
KeywordsRecommender systems Case-based reasoning Problem solving Case retrieval
- 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
- 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
- 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
- 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
- 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
- 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 http://docs.lib.purdue.edu/ijpbl/vol2/iss2/4.
- Kolodner, J. (1991). Improving human decision making through case-based decision aiding. AI Magazine, 12(2), 52–68.Google Scholar
- 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
- Lazonder, A., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 87(4), 1–38.Google Scholar
- 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 http://docs.lib.purdue.edu/ijpbl/vol3/iss1/3.
- 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
- Schon, D. A. (1984). The reflective practitioner: How professionals think in action (1st ed.). New York: Basic Books.Google Scholar
- 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
- 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
- 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
- Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.Google Scholar
- 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