Learning Content Recommendations on Personalized Learning Environment Using Collaborative Filtering Method

Personal Learning Environment (PLE) is an e-learning concept that allows users to manage their learning environment both in terms of content and process. However, significant problems with PLE implementation in distance learning are excessive information and difficulties in finding the suitable learning content for learners. To overcome these problems, an experimental study was conducted to explore a learning content recommendation system for learners. The learning content recommendation system uses the Collaborative Filtering (CF) algorithm for the basis. CF is a method for filtering information by collecting ratings and combining it with similar information needs or interests of other users. This study intends to build the concept of PLE distance learning by applying the CF recommendation system to find learning content that is appropriate to the needs of learners. The test results reveal that the proposed PLE application is compliant with the PLE attributes. This study has also succeeded in applying a recommendation system using the CF algorithm with the concept of PLE in distance learning. Moreover, the Mean Absolute Error (MAE) calculation reveals that the best-obtained recommendation results reached by K=10. Based on the experimental data obtained, the greater the value of K used in the CF algorithm, the greater the average error.