Natural Language Understanding for the Recommendation of Learning Resources Within Student Collaboration Tools

Discussion forums are popular tools in Massive Open Online Courses (MOOCs), used by students to express feelings, exchange ideas, and ask for help. Unfortunately, the huge number of enrolled students hinders educational scaffolding activities, including the active participation of instructors in discussion forums. Therefore, students seeking to clarify the concepts learned may not receive the answers they need, reducing engagement and promoting dropout. This work presents a methodology for supporting learners within discussion forums, by analyzing conversations among students and providing them with recommendations in terms of relevant learning resources. The methodology involves several steps: the initial definition of an ontology that details the topics of the course, the real-time analysis of student posts within the discussion forums to extract different attributes including intent of the post, the concepts it is about, the sentiment, and the level of urgency and confusion. The extracted information is then used by a rules-based mechanism to assess whether the learner needs a recommendation. If so, the system suggests the most suitable learning resources. The paper also includes an initial evaluation of the proposed methodology.