Recommender System for Integrating Tacit Knowledge in E-learning Environment to Enhance Learning Potential of Learners

Abstract

New technologies and fast expansion of internet have made access to information easier for learners in web-based education. E-learning environments today are used largely to handcraft training courses about thoughtfully selected topics for delivery to learners registered for those courses. E-learning environments are online so it is not mandatory for learners and instructors to meet in the same place and these e-learning environments play an important role in self-learning and self-training of learners. Educational organizations nowadays adapting e-learning platforms for their learners so as they can gradually get benefited and enhance their learning capabilities. The recommender system helps the learners to overcome information overload problem as learners are recommended e-learning resources according to their habits, likes & dislikes, learning styles, interest area and their level of knowledge.The paper presents a novel approach, a framework for building architecture for a recommender system that integrates tacit knowledge in e-learning environment so as to enhance the learning potential of learners. The proposed approach is based on Nonaka and Takeuchi’s knowledge based SECI model of learning that twirl around inter-transformation of two forms of knowledge that are explicit and tacit. The proposed framework adapts SECI model and contains four modules, first a learner module which contains learners’ personal information, their learning style and their interest area that is uses to identify learners learning preferences and activities. Second a domain module which contains all the information for a particular area and holds the knowledge about the set of course structure. Third an e-assessment module that consists of three blocks named diagnostic assessment, formative assessment and revision module, the main intention of is to provide more benefits to the learners to improve their knowledge. The last module is recommendation module that helps to generate the recommendation of suitable learning module to the learners, founded on their learning style and their level of knowledge.