Automated Feedback Generation for Argument-Based Intelligent Tutoring Systems

Guid, Matej; Pavlič, Matevž; Možina, Martin

doi:10.5220/0007717600700077

Proceedings of the 11th International Conference on Computer Supported Education 2019

DOI: 10.5220/0007717600700077

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Automated Feedback Generation for Argument-Based Intelligent Tutoring Systems

Matej Guid

Matevž Pavlič

Martin Možina

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2023

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ML-Based Teaching Systems: A Conceptual Framework

Spitzer,

Kühl,

Heinz

et al. 2023

Proc. ACM Hum.-Comput. Interact.

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As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and pass it on to novices. While this knowledge transfer has traditionally occurred through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to understand better and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda.

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ML-Based Teaching Systems: A Conceptual Framework

Spitzer,

Kühl,

Heinz

et al. 2023

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

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Automated Feedback Generation for Argument-Based Intelligent Tutoring Systems

Cited by 1 publication

References 3 publications

ML-Based Teaching Systems: A Conceptual Framework

ML-Based Teaching Systems: A Conceptual Framework

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