The recommender system has gained research attention from education research communities mainly due to two main reasons: increasing needs for personalized learning and big data availability in the education sector. This paper presents a hybrid user-collaborative, rule-based filtering recommendation system for education context. User profiles are represented by learning outcome scores and contextual information. The user-collaborative filtering method is used for predicting the targeted student’s learning outcome of a particular course. The predicted learning outcome combined with a set of decision rules are used for recommending some relevant link of learning materials to the targeted student. The initial contextual information which is assessed during the online program entrance test makes it possible for the proposed recommneder system to give automated recommendations to new students. The proposed method was tested using student learning outcome records from BINUS Online repository data. The results of performance evaluation of both recommender system with contextual information which achieves 458.22 MSE and the recommender system without contextual information which achieves 413.19 MSE are not significantly different. However, unlike the latter recommender system, the former recommender system has an advantage mainly that it can be used to give recommendation to the targeted students since their early program stage.

推荐器系统已得到教育研究界的研究关注，主要是由于两个主要原因：教育部门对个性化学习的需求增加以及大数据可用性。本文提出了一种用于教育情境的混合用户，基于规则的协作过滤推荐系统。用户配置文件由学习结果分数和上下文信息表示。用户协作过滤方法用于预测目标学生特定课程的学习结果。预测的学习结果与一组决策规则结合在一起，用于向目标学生推荐学习材料的一些相关链接。在线课程入学测试期间评估的初始上下文信息使建议的推荐系统可以自动向新学生提供建议。使用来自BINUS Online存储库数据的学生学习结果记录对提出的方法进行了测试。具有上下文信息的推荐器系统达到458.22 MSE的性能评估结果与没有上下文信息的推荐器系统达到413.19 MSE的性能评估结果没有显着差异。但是，与后者的推荐系统不同，前者的推荐系统的主要优点是，自计划初期起，就可以用于向目标学生提供推荐。使用来自BINUS Online存储库数据的学生学习结果记录对提出的方法进行了测试。具有上下文信息的推荐器系统达到458.22 MSE的性能评估结果与没有上下文信息的推荐器系统达到413.19 MSE的性能评估结果没有显着差异。但是，与后者的推荐系统不同，前者的推荐系统的主要优点是，自计划初期起就可用于向目标学生提供推荐。使用来自BINUS Online存储库数据的学生学习结果记录对提出的方法进行了测试。具有上下文信息的推荐器系统达到458.22 MSE的性能评估结果与没有上下文信息的推荐器系统达到413.19 MSE的性能评估结果没有显着差异。但是，与后者的推荐系统不同，前者的推荐系统的主要优点是，自计划初期起，就可以用于向目标学生提供推荐。