CAR-based Personalized Learning Activity Recommendations for Medical Interns

ABSTRACT

Clinical education has a great impact on the health care quality. In a hospital, clinical teachers often devote much time to provide services for patients in the pressed health care environment. Clinicians have less or no time to teaching clinical courses, discussing clinical cases with medical interns. In this study, we setup an electronic learning (e-learning) platform, a learning management system (LMS) in a hospital as a complement to clinical education for internship. Interns could learn and discuss with their classmates any time. Despite LMS could assist clinical training and provide the collaborative environment, however, students only have the same user interfaces even though they have the different learning preferences on LMS. Typical LMS could not provide the personalized learning activities for students.

Recommender system could provide the personalized services to customers according to their interest, which are often used in electronic commerce (EC). In this study, we propose a cluster association rules (CAR) based method to recommend the personalized activities to students on LMS. First, students were clustered into two groups, active and inactive groups. In each group, student learning behavior pattern, i.e., activity association rules and most frequent activities were derived from LMS. Finally, the activities were recommended to the target student, which were sorted by the confidences of the association rules and the frequencies of the activities. The experiment results demonstrate that the proposed CAR-based method performs better than the typical collaborative filtering (CF) method over all top N recommendations. CAR-based method could provide the better personalized activities to students than the traditional CF method.

References

1. Agrawal, R., Imielinski, T., and Swami, A., 1993. Mining association rules between sets of items in large databases. In Proceedings of the Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. (Washington, D.C., United States1993), ACM, 207--216.
2. Balabanović, M. and Shoham, Y., 1997. Fab: content-based, collaborative recommendation. Communications of the ACM 40, 3, 66--72.
3. Black, A.D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., Mckinstry, B., Procter, R., Majeed, A., and Sheikh, A., 2011. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS medicine 8, 1, e1000387.
4. Bobadilla, J., Serradilla, F., and Hernando, A., 2009. Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems 22, 4, 261--265.
5. Chassin, M.R. and Galvin, R.W., 1998. The urgent need to improve health care quality: Institute of Medicine National Roundtable on Health Care Quality. Jama 280, 11, 1000--1005.
6. Chu, S.K., Chan, C.K., and Tiwari, A.F., 2012. Using blogs to support learning during internship. Computers & Education 58, 3, 989--1000.
7. Eysenbach, G., 2001. What is e-health? J Med Internet Res 3, 2, e20.
8. Gormley, G.J., Collins, K., Boohan, M., Bickle, I.C., and Stevenson, M., 2009. Is there a place for e-learning in clinical skills? A survey of undergraduate medical students' experiences and attitudes. Medical teacher 31, 1, e6--e12.
9. Hill, W., Stead, L., Rosenstein, M., and Furnas, G., 1995. Recommending and evaluating choices in a virtual community of use. In Proceedings of the Proceedings of the SIGCHI Conference on Human factors in Computing Systems (Denver, Colorado, USA1995), ACM Press/Addison-Wesley Publishing, 194--201.
10. Klašnja-Milićević, A., Vesin, B., Ivanović, M., and Budimac, Z., 2011. E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education 56, 3, 885--899.
11. Lang, K., 1995. Newsweeder: Learning to Filter Netnews. In Proc. 12th Int'l Conf. Machine Learning.
12. Li, J. and Za Ane, O., 2004. Combining Usage, Content, and Structure Data to Improve Web Site Recommendation. In E-Commerce and Web Technologies, K. Bauknecht, M. Bichler and B. PR LL Eds. Springer Berlin Heidelberg, 305--315. DOI= http://dx.doi.org/10.1007/978-3-540-30077-9_31.
13. Liang, G., Weining, K., and Junzhou, L., 2006. Courseware Recommendation in E-Learning System. In Advances in Web Based Learning -- ICWL 2006, W. LIU, Q. LI and R. W.H. LAU Eds. Springer Berlin Heidelberg, 10--24. DOI= http://dx.doi.org/10.1007/11925293_2.
14. Lin, Q.-Y., Chen, Y.-L., Chen, J.-S., and Chen, Y.-C., 2003. Mining inter-organizational retailing knowledge for an alliance formed by competitive firms. Information & Management 40, 5, 431--442.
15. Macqueen, J., 1967. Some methods for classification and analysis of multivariate observations. In Fifth Berkeley Symposium on Mathematical Statistics and Probability University of California Press, Statistical Laboratory of the University of California, Berkeley, 281--297.
16. Manouselis, N., Vuorikari, R., and Van Assche, F., 2010. Collaborative recommendation of e-learning resources: an experimental investigation. Journal of Computer Assisted Learning 26, 4, 227--242. DOI= http://dx.doi.org/10.1111/j.1365-2729.2010.00362.x.
17. Peng, C.-C. and Liu, D.-R., 2010. Combining reputation and content-based filtering for blog article recommendation in social bookmarking websites. In Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business ACM, 8--14.
18. Prideaux, D., Alexander, H., Bower, A., Dacre, J., Haist, S., Jolly, B., Norcini, J., Roberts, T., Rothman, A., and Rowe, R., 2000. Clinical teaching: maintaining an educational role for doctors in the new health care environment. Medical education 34, 10, 820--826.
19. Punj, G. and Stewart, D.W., 1983. Cluster analysis in marketing research: review and suggestions for application. Journal of Marketing Research 20, 134--148.
20. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (Chapel Hill, North Carolina, USA1994), ACM, 175--186.
21. Ruiz, J.G., Mintzer, M.J., and Leipzig, R.M., 2006. The impact of e-learning in medical education. Academic medicine 81, 3, 207--212.
22. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., 2000. Analysis of recommendation algorithms for e-commerce. In Proceedings of the Proceedings of the Second ACM Conference on Electronic Commerce (Minneapolis, Minnesota, USA2000), ACM, 158--167.
23. Seluakumaran, K., Jusof, F.F., Ismail, R., and Husain, R., 2011. Integrating an open-source course management system (Moodle) into the teaching of a first-year medical physiology course: a case study. Advances in physiology education 35, 4, 369--377.
24. Shardanand, U. and Maes, P., 1995. Social information filtering: algorithms for automating "word of mouth". In Proceedings of the Proceedings of the SIGCHI conference on Human factors in computing systems (Denver, Colorado, USA1995), ACM/Addison-Wesley Publishing, 210--217.
25. Tang, T. and Mccalla, G., 2005. Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning 4, 1, 105--129.
26. Van Rijsbergen, C.J., 1979. Information retrieval. Butterworth-Heinemann Newton, MA, USA.

Index Terms

• Applied computing

  • Education

  • Life and medical sciences