Article
Version 1
Preserved in Portico This version is not peer-reviewed
Learning Analytics in the Era of Large Language Models
Version 1
: Received: 3 August 2023 / Approved: 3 August 2023 / Online: 4 August 2023 (07:23:55 CEST)
A peer-reviewed article of this Preprint also exists.
Mazzullo, E.; Bulut, O.; Wongvorachan, T.; Tan, B. Learning Analytics in the Era of Large Language Models. Analytics 2023, 2, 877-898. Mazzullo, E.; Bulut, O.; Wongvorachan, T.; Tan, B. Learning Analytics in the Era of Large Language Models. Analytics 2023, 2, 877-898.
Abstract
Although learning analytics (LA) holds great potential to improve teaching and learning, LA research and practice are currently riddled with limitations that impact every stage of the LA life cycle. The present paper offers an overview of these challenges before proposing strategies to overcome them and exploring how the recent innovations brought forth by language models can improve LA research and practice. In particular, we encourage the empowerment of teachers during LA development, as this would strengthen the theoretical foundation of LA solutions and increase their interpretability and usability. Furthermore, we provide examples of how process data can be used to understand learning processes and generate more interpretable LA insights. Furthermore, we explore how LLMs could come into play in LA to generate interpretable insights, timely and actionable feedback, increase personalization, and support teachers’ tasks more broadly.
Keywords
learning analytics; large language model; artificial intelligence; process data
Subject
Social Sciences, Education
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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