Utilising learning analytics for study success: A systematic review of five years of research (2013-17)

Yau, Jane Yin-Kim ; Mah, Dana-Kristin ; Ifenthaler, Dirk

Document Type: Conference presentation
Year of publication: 2018
Conference title: European First Year's Experience Conference EFYE 2018
Location of the conference venue: Utrecht, Netherlands
Date of the conference: 25-27.06.2018
Publication language: English
Institution: Business School > Wirtschaftspädagogik, Technologiebasiertes Instruktionsdesign (Ifenthaler 2015-)
Subject: 004 Computer science, internet
Abstract: Mining data for insights to improve education enables a new level of evidence-based research into learning and teaching. Currently, promising learning analytics applications are being developed which utilise data produced in the educational context. Learning analytics show promise to enhance student success in higher education (Arnold & Pistilli, 2012). The purpose of this study was to examine the utilization of learning analytics to support study success particularly applying to first year university students. Our main research question was to identify whether there is a link between learning analytics and the respective intervention measures to increase study success. Our systematic review consisted of empirical studies conducted during the past five years (2013-2017). Search terms identified 6220 papers from various sources (e.g., Scopus, ERIC). After duplicated articles were removed, there were 3163 papers remaining. Each paper was screened and the inclusion criteria limited the key studies to 380. Findings indicate that robust empirical evidence supporting the effectiveness of learning analytics are still lacking. The preliminary results obtained so far suggest that there is a considerable number of sophisticated learning analytics tools which utilise effective techniques in predicting at-risk students. However, learning analytics applications focussing on personalised and adaptive support for learning are lacking. The latest study reveals evidence in the main successful application of learning analytics to reduce student dropout can only be found in the USA, Australia, and England (e.g., Ferguson & Clow, 2017). However, study success may not be exclusively the result of the use of learning analytics but also some additional means of technological or institutional support. More work on ethical and privacy guidelines supporting a wider adoption of learning analytic systems is required as well as work towards a standardized and personalized learning analytics system which can be integrated into any learning environment providing reliable at-risk student prediction and intervention strategies. Elaborate details and extensive analysis of the results of this systematic review will be presented in this paper session. We analysed international empirical evidence and will present the lessons learnt, limitations of learning analytics to support study success, and challenges of future work in this research area.

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