University of Oulu

Gedrimiene, E., Celik, I., Mäkitalo, K., & Muukkonen, H. (2023). Transparency and Trustworthiness in User Intentions to Follow Career Recommendations from a Learning Analytics Tool. Journal of Learning Analytics, 10(1), 54-70.

Transparency and trustworthiness in user intentions to follow career recommendations from a learning analytics tool

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Author: Gedrimiene, Egle1; Celik, Ismail1; Mäkitalo, Kati1;
Organizations: 1Faculty of Education and Psychology, University of Oulu, Pentti Kaiteran katu1, 90570, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
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Language: English
Published: Society for Learning Analytics Research, 2023
Publish Date: 2023-09-22


Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their users and how perceptions of these variables relate to user behaviour. In this study, we investigate user experiences of an LA tool in the context of career guidance, which plays a crucial role in supporting nonlinear career pathways for individuals. We review the ethical challenges of big data, AI, and LA in connection to career guidance and analyze the user experiences (N = 106) of the LA career guidance tool, which recommends study programs and institutions to users. Results indicate that the LA career guidance tool was evaluated as trustworthy but not transparent. Accuracy was found to be a stronger predictor for the intention to follow on the recommendations of the LA guidance tool than was understanding the origins of the recommendation. The user’s age emerged as an important factor in their assessment of transparency. We discuss the implications of these findings and suggest emphasizing accuracy in the development of LA tools for career guidance.

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Series: Journal of learning analytics
ISSN: 1929-7750
ISSN-E: 1929-7750
ISSN-L: 1929-7750
Volume: 10
Issue: 1
Pages: 54 - 70
DOI: 10.18608/jla.2023.7791
Type of Publication: A1 Journal article – refereed
Field of Science: 516 Educational sciences
113 Computer and information sciences
Funding: The publication of this article received financial support from the European Commission (grant SI2.488704 [ECOKT2016-1]) Learner-centred digital ecosystem of competence development, the Finnish Ministry of Education and Culture AnalytiikkaÄly Project (grant number OKM/272/523/2017), and a personal grant from Finnish Cultural Foundation.
Copyright information: © The Author(s) 2023. The Journal of Learning Analytics. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.