Utilising learning analytics to support study success in higher education: a systematic review

Abstract

Study success includes the successful completion of a first degree in higher education to the largest extent, and the successful completion of individual learning tasks to the smallest extent. Factors affecting study success range from individual dispositions (e.g., motivation, prior academic performance) to characteristics of the educational environment (e.g., attendance, active learning, social embeddedness). Recent developments in learning analytics, which are a socio-technical data mining and analytic practice in educational contexts, show promise in enhancing study success in higher education, through the collection and analysis of data from learners, learning processes, and learning environments in order to provide meaningful feedback and scaffolds when needed. This research reports a systematic review focusing on empirical evidence, demonstrating how learning analytics have been successful in facilitating study success in continuation and completion of students' university courses. Using standardised steps of conducting a systematic review, an initial set of 6220 articles was identified. The final sample includes 46 key publications. The findings obtained in this systematic review suggest that there are a considerable number of learning analytics approaches which utilise effective techniques in supporting study success and students at risk of dropping out. However, rigorous, large-scale evidence of the effectiveness of learning analytics in supporting study success is still lacking. The tested variables, algorithms, and methods collected in this systematic review can be used as a guide in helping researchers and educators to further improve the design and implementation of learning analytics systems.

Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners

“…More recently, research on learning analytics has seen an extensive diversification of initial learning analytics approaches (Prieto et al 2019). However, an emphasis on supporting learning processes whenever the learner needs it appears to be underrepresented (Ifenthaler and Yau 2020). Therefore, this project critically reflected on how to put learning at the centre of learning analytics by (a) undertaking a literature review focussing on the impact of learning analytics on supporting learning and teaching, and, (b) conducting a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics In the discussion section we will summarise the findings, (1) reporting the identified alignment issues and challenges, (2) suggesting six strategies and actions for involved stakeholders, and (3) outlining a possible research agenda for closing identified research gaps.…”

“…Graphicacy refers to the way in which spatial information is communicated other than by words or numbers alone (Boardman 1990). Educational data literacy is defined as the ethically responsible collection, management, analysis, comprehension, interpretation, and application of data from educational contexts (Ifenthaler and Yau 2020). With more and more learning analytics systems becoming available, some teachers may start using data to inform their practice while their learners may have access to analytics performance information that may help them set their own pace and objectives.…”

“…To sum up the literature, positive evidence has been found on the use of learning analytics to support learning (Ifenthaler and Yau 2020). However, in terms of practical learning analytics, further evidence is required not only about the effects of learning analytics systems (e.g.…”

Implantable and Wearable Neuroengineering Education: A Review of Postgraduate Programmes

“…We have recently demonstrated in the literature that there are many traditional engineering undergraduate programmes that provide a basic understanding of neuro-engineering, without providing a sound understanding of communications, signal processing, microelectronics and integrated circuits for wearables and implantables. Similar to the methodology described in [53] and [16], we first defined the research questions and the inclusion criteria of our search. Second, we selected relevant programmes that met some of these criteria.…”

“…Again, similar to the methods using in [16], [53], we developed search strings using Boolean operators (AND, OR) to connect these descriptors.…”