Learner’s intention analysis to mitigate the cold start problem in personalized learning recommendation systems

Understanding the learner’s requirements and status is important for recommending relevant and appropriate learning materials to the learner in personalized learning. For this purpose, the learning recommendation system must include the learner’s contextual and behavioral information. However, in the case of cold start problems, the system has no prior information about a new user. Moreover, the system cannot acquire the required information instantly because the automated and instantaneous acquisition of this information is not trivial. This hinders the personalized recommendation for a new learner. Engaging the learner in a questionnaire session may allow the learning system to acquire most of the required information. However, this makes the learner uninterested and disengaged with the system. To address this, we propose a recommendation system for personalized learning that mitigates the cold start problem by acquiring important information by analyzing the learner’s questions submitted to the learning system. By analyzing the meta-information of a question, we identified the learner’s exact requirements, domain and topic knowledge, and expected knowledge gain from the learning material. Ontology, BiLSTM, and context mapping are used for learner modelling and topic classification, and identifying suitable learning materials, respectively. Furthermore, MCDM techniques are used for ranking the learning materials. Our experimental assessment and validation suggest that the information acquired based on our proposed approach is sufficient for understanding a new learner’s needs and suitability and, accordingly, for recommending suitable personalized learning materials.