AI evidence extraction

The Interplay of Learning Analytics and Artificial Intelligence

Jelena Jovanović · Annals of Computer Science and Information Systems · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

1.57

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.15439/2024f5859

Methodology & findings

Study design

Narrative literature review relying on examination of latest empirical research in Learning Analytics, AI in Education, and Educational Data Mining, using the cyclical model of LA as an exploration framework..

Main result

The paper examines how "key components of the LA model—namely data, methods, and actions—relate to and may benefit from the latest developments in AI, and especially Generative AI." The review identifies that while Learning Analytics has made significant contributions to understanding learning environments, "new opportunities and challenges continue to emerge with the ever-changing modalities of teaching and learning, the latest of which are associated with the rapid development and accessibility of Artificial Intelligence (AI)."

Reports effect sizes.

Research paradigm

Mixed methods (interpretive and empirical)

Author conclusions

The authors conclude that the widespread use of digital systems has enabled "collecting, measuring, and analysing data about user (learner, teacher) interactions with a variety of learning resources and activities, with the ultimate objective of better understanding learning and advancing both learning outcomes and the overall learning experience." They frame their examination as "aiming for evidence-based analysis and discussion of the interplay between LA and AI" by relying on "the latest empirical research in LA and the related research fields of AI in Education and Educational Data Mining."

Open questions raised

The paper identifies that many open questions and challenges remain in Learning Analytics, and that new opportunities and challenges continue to emerge with evolving teaching and learning modalities, particularly those associated with the rapid development and accessibility of Artificial Intelligence.
The paper identifies gaps regarding the relationship between Learning Analytics components (data, methods, and actions) and Artificial Intelligence developments, particularly Generative AI. It notes that many open questions and challenges remain in LA, and that new opportunities and challenges continue to emerge with changing teaching and learning modalities.
Many open questions and challenges remain in Learning Analytics; new opportunities and challenges emerge with changing teaching and learning modalities, particularly with rapid development and accessibility of AI and Generative AI.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 78%

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