Predicting Quality of Answer in Collaborative Question Answer Learning

Abstract

Studies over the years shown that students had actively and more interactively involved in a classroom discussion to gain their knowledge. By posting questions for other participants to answer, students could obtain several answers to their question. The problem is sometimes the answer chosen by student as the best answer is not necessarily the best quality answer. Therefore, an automatic recommender system based on student activity, may improve these situations as it will choose the best answer objectively. On the other side, in the implementation of collaborative learning, in addition to sharing information, sometimes students also need a reference or domain knowledge which relevant with the topic. In this paper, we proposed answer quality predictor in collaborative question answer (CQA) learning, to predict the quality of answer either from recommender system based on users activity or domain knowledge as reference information.