NDLI: CAR-based Personalized Learning Activity Recommendations for Medical Interns

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CAR-based Personalized Learning Activity Recommendations for Medical Interns

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CAR-based Personalized Learning Activity Recommendations for Medical Interns

Content Provider	ACM Digital Library
Author	Liou, Chuen-He Chen, Hsing-Shun
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.
Starting Page	49
Ending Page	55
Page Count	7
File Format	PDF
ISBN	9781450326186
DOI	10.1145/2617848.2617854
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Clinical education E-learning Collaborative filtering Intern Association rule Activity Clustering Recommender system
Content Type	Text
Resource Type	Article

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