Learning Analytics for Peer Assessment: A Scoping Review

Misiejuk, Kamila; Wasson, Barbara

doi:10.1007/978-3-031-29411-2_2

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…LA is an interdisciplinary field that encompasses several disciplines, including education, psychology and computer science, and its analytical methods include both quantitative and qualitative approaches (Khalil & Ebner, 2015; Misiejuk & Wasson, 2017). The LA ecosystem has been proposed as a lifecycle that includes the collection, analysis, reporting and sharing of learning data (Khalil & Ebner, 2015; Khokhlova, 2023).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Understanding privacy and data protection issues in learning analytics using a systematic review

Liu,

Khalil

2023

Brit J Educational Tech

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The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross‐regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence‐based, thereby increasing the trustworthiness of learning analytics and its usefulness. Practitioner notesWhat is already known about this topic Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics. Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions. What this study contributes Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle. Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views. Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics. Implications for practice and/or policy Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics. Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders. Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.

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Section: Background and Literature Reviewmentioning

confidence: 99%