An Adaptive Course Recommender System for Teachers' Professional Development

• In Event: 30.057 - Learning Analytics and Data Mining to Improve Student Learning and Teacher Professional Development

Abstract

Learning management systems are typically used by teacher professional development to support teachers in managing and administrating training courses. However, teacher learning management systems are rarely adaptive. With the increasing amount of training courses, course selection becomes a difficult and time-consuming problem. In this study, we focused on the design and development of an adaptive course recommender system, which was integrated into Shanghai Teacher Learning Management System. Model-based collaborative filtering recommendation techniques with machine learning and data mining methods were used. The online methods, offline methods and user studies were designed to evaluate the recommender system. Results and implications are discussed.

Authors

• Fengwei Sui

• J. Michael Spector, University of North Texas

• Youqun Ren, East China Normal University

• Lijia Lin, East China Normal University

• Huaihao Zhang, Shanghai Teacher Training Center - Shanghai

• Yi Zhan, East China Normal University

• Layne Peng