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

Qinyi Liu  Mohammad Khalil 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(6):1715-1747

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 notes

What 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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Leveraging complexity frameworks to refine theories of engagement: Advancing self-regulated learning in the age of artificial intelligence     

Jonathan C. Hilpert  Jeffrey A. Greene  Matthew Bernacki 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1204-1221

Capturing evidence for dynamic changes in self-regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected from n = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control, n = 48), (2) perform poorly and received intervention (treatment, n = 95) and (3) perform well (not flagged, n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments.

Practitioner notes

What is already known about this topic

• Self-regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.
• SRL is a dynamic, temporal process that leads to purposeful student engagement.
• Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.

What this paper adds

• A Markov process for measuring dynamic SRL processes using log data.
• Evidence that dynamic, interaction-dominant aspects of SRL predict student achievement.
• Evidence that SRL processes can be meaningfully impacted through educational intervention.

Implications for theory and practice

• Complexity approaches inform theory and measurement of dynamic SRL processes.
• Static representations of dynamic SRL processes are promising learning analytics metrics.
• Engineered features of LMS usage are valuable contributions to AI models.

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