Personalized recommendation for learning resources based-on case reasoning agents

Ample online resources for e-learning provide students with choices and initiative, which however results in much challenge in matching the needs of students with different backgrounds and learning preferences due to information overload. Facing diverse learning resources, students have difficulties in making appropriate choices to meet their learning objectives. This paper proposes a framework of multi-agents collaboration case-based reasoning (MACBR) for personalized recommendations of e-learning resources, taking into account of characteristics of the learner. The paper firstly presents a workflow of Case-based Reasoning (CBR) for learning resource recommendation, and then proposes the collaboration framework of MACBR, finally illustrates the application of MACBR for personalized recommendation in e-learning.