Personalized and adaptive e-learning systems for semantic Web: a systematic review and roadmap

Abstract

Purpose The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems. Design/methodology/approach Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners. Findings The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes. Research limitations/implications The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process. Social implications E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status. Originality/value A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.