Learning analytics techniques: an overview and future research possibilities

Purpose The review aims to synthesize previous studies to present an overview of the techniques commonly used in learning analytics, as well as identify possible knowledge gaps in the extant studies and provide insights on future directions for learning analytics techniques moving forward. Design/methodology/approach This paper provides a systematic review of learning analytics techniques. A total of 63 articles were included in the final review and 3 main themes emerged based on our research questions. These themes include (A) individual learning, (B) collaborative learning and (C) game-based learning. The first theme is related to the application of learning analytics techniques in the context of individual student learning, while the second and third themes focus on the application of learning analytics techniques in the context of collaborative learning and game-based learning research, respectively. The paper summarizes key findings, identifies possible gaps for future research and provides recommendations for future research. Findings The commonly used techniques include classification, content analysis, social network analysis and taxonomic mapping. Multimodal learning analytics, which uses data from multiple sources to understand learners’ behavior and experience, is also growing. The review of learning analytics research highlights several knowledge gaps, including methodological issues, adaptability of techniques, ethical, risk and privacy concerns and precise terminologies for methodological decisions. The choice of learning analytics techniques should be guided by research questions and data nature. Originality/value This work meets the originality requirement.