Towards Interpretability of Attention-Based Knowledge Tracing Models

F., Rodrigues, Thales B. S.; Souza, Jairo; Bernardino, Heder S.; Baker, Ryan S.

doi:10.5753/sbie.2022.224685

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI can bring up a new idea or observation, drawing attention to something not noticed before, or the learner might prompt the AI to look for and then share something new to consider (Montaner et al, 2003). Related examples include an 'alerting dashboard' for improving scientific inquiry (Dickler et al, 2021) and research on knowledge tracing of attention (Rodrigues et al, 2022).…”

Section: Ai As a Partner In Individual Exploration Learning And Expre...mentioning

confidence: 99%

Learning theories for artificial intelligence promoting learning processes

Gibson

Kovanović

Dexter

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities. Practitioner notesWhat is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning. Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified. Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices. What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education. A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels. The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems. Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

show abstract

Section: Ai As a Partner In Individual Exploration Learning And Expre...mentioning

confidence: 99%