100 DAYS OF... Data for Learning | Journal Club | 17 January

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What does research tell us about data for learning?

Date. 17 January 2023.
Time. 12:30 - 13:30.
Location. Online.
Scope. Privacy; Ethics; Checklist for researchers, policy makers and institutional managers; facilitating trusted implementation of Learning Analytics.

In the “100 DAYS OF... Data for Learning” Journal Clubs we explore and discuss papers/research on data for learning in the context of (engineering) education. Following a flipped-classroom approach, participants read an article before each session (the article will be announced latest one week before the journal club date) and start an open, moderated, discussion on the article.

In this series of events, the core question to be addressed is:
What does research on data for learning tell us about (designing) our engineering education?

To continue our exploration of data for learning in (engineering) education, the Journal Club is discussing another interesting article on 17 January 

Drachsler, H., & Greller, W. (2016, April). Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 89-98).

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Abstract
The widespread adoption of Learning Analytics (LA) and Educational Data Mining (EDM) has somewhat stagnated recently, and in some prominent cases even been reversed following concerns by governments, stakeholders and civil rights groups about privacy and ethics applied to the handling of personal data. In this ongoing discussion, fears and realities are often indistinguishably mixed up, leading to an atmosphere of uncertainty among potential beneficiaries of Learning Analytics, as well as hesitations among institutional managers who aim to innovate their institution’s learning support by implementing data and analytics with a view on improving student success. In this paper, we try to get to the heart of the matter, by analysing the most common views and the propositions made by the LA community to solve them. We conclude the paper with an eight-point checklist named DELICATE that can be applied by researchers, policy makers and institutional managers to facilitate a trusted implementation of Learning Analytics.