The End is the Beginning is the End

The closed-loop learning analytics framework

Bibliographic Data

ID21563341
AuthorsMichael Sailer (0000-0001-6831-5429, University of Augsburg), Manuel Ninaus (0000-0002-4664-8430, University of Graz, corresponding author), Stefan E Huber (0000-0001-5057-0376, University of Graz), Elisabeth Bauer (0000-0003-4078-0999, Technical University of Munich), Samuel Greiff (0000-0003-2900-3734, University of Luxembourg)
Year2024
Volume158
Pages108305
Publication date2024-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueComputers in Human Behavior (JOURNAL)
Journal identifiersISSN: 0747-5632 • E-ISSN: 1873-7692
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.chb.2024.108305
OpenAlexW4396950990
LanguageEN
Citations received14
References cited167

This article provides a comprehensive review of current practices and methodologies within the field of learning analytics, structured around a dedicated closed-loop framework. This framework effectively integrates various aspects of learning analytics into a cohesive framework, emphasizing the interplay between data collection, processing and analysis, as well as adaptivity and personalization, all connected by the learners involved and underpinned by educational and psychological theory. In reviewing each step of the closed loop, the article delves into the advancements in data collection, exploring how technological progress has expanded data collection methods, particularly focusing on the potential of multimodal data acquisition and how theory can inform this step. The processing and analysis step is thoroughly reviewed, highlighting a range of methods including machine learning and AI, and discussing the critical balance between prediction accuracy and interpretability. The adaptivity and personalization step examines the current state of research, underscoring significant gaps and the necessity for theory-informed, personalized learning interventions. Overall, the article underscores the importance of interdisciplinarity in learning analytics, advocating for the integration of insights from various fields to address challenges such as ethical data usage and the creation of quality learning experiences. This framework and review aim to guide future research and practice in learning analytics, promoting the development of effective, learner-centric educational environments driven by balancing data-driven insights and theoretical understanding

Analytics · Closed loop · Control engineering · Data science · Dead end · End user · End-to-end principle · World Wide Web · Big Data and Business Intelligence · Computer Science · Engineering · Intelligent Tutoring Systems and Adaptive Learning · Mathematics · Online Learning and Analytics · Psychology · Social Psychology · Artificial Intelligence

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Open DOIOpen Access