An Adaptive Course Recommender System For Teachers' Professional Development

In-service teacher training is an important way to promote the professional development of in-service teachers,and learning management systems are typically used by teacher professional development to support teachers in managing and administrating training courses.With the system,in-service teachers could easily browse,query,register and evaluate 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.Recommender systems have emerged as an essential method to assist users in finding desirable products from large sets of available products,which have been widely used in areas such as e-commerce,Journalism and movies.Unlike traditional search and navigation functions in teacher learning management systems,recommender systems could infer and satisfy the individual learners' needs by actively pushing courses to in-service teachers with the ability of discovering potential patterns in data.In this study,we focused on the design and development of an adaptive course recommender system,which was integrated into Teacher Learning Management System.Firstly,the reason and significant to study the topic were discussed.Secondly,adaptive technologies were analyzed,also different recommender algorithms were introduced,laying a theoretical foundation for the following.Thirdly,learner model and adaptive model were built,and adaptive course recommender system framework was designed.Then,data preprocessing,storage and evaluation modules were developed,and model-based collaborative filtering recommendation techniques with machine learning and data mining methods were used to build the recommender system.Finally,online methods and offline methods were designed to evaluate the recommender system.Results and implications were discussed.Moreover,innovation and deficiency of the study were summarized,and future research directions were prospected.