When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908414581407744 |
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| author | Chen, Angxuan |
| author_facet | Chen, Angxuan |
| contents | This study investigated the impact of a theory-driven, explainable Learning Analytics Dashboard (LAD) on university students' human-AI collaborative academic abstract writing task. Grounded in Self-Regulated Learning (SRL) theory and incorporating Explainable AI (XAI) principles, our LAD featured a three-layered design (Visual, Explainable, Interactive). In an experimental study, participants were randomly assigned to either an experimental group (using the full explainable LAD) or a control group (using a visual-only LAD) to collaboratively write an academic abstract with a Generative AI. While quantitative analysis revealed no significant difference in the quality of co-authored abstracts between the two groups, a significant and noteworthy difference emerged in conceptual understanding: students in the explainable LAD group demonstrated a superior grasp of abstract writing principles, as evidenced by their higher scores on a knowledge test (p= .026). These findings highlight that while basic AI-generated feedback may suffice for immediate task completion, the provision of explainable feedback is crucial for fostering deeper learning, enhancing conceptual understanding, and developing transferable skills fundamental to self-regulated learning in academic writing contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard Chen, Angxuan Human-Computer Interaction This study investigated the impact of a theory-driven, explainable Learning Analytics Dashboard (LAD) on university students' human-AI collaborative academic abstract writing task. Grounded in Self-Regulated Learning (SRL) theory and incorporating Explainable AI (XAI) principles, our LAD featured a three-layered design (Visual, Explainable, Interactive). In an experimental study, participants were randomly assigned to either an experimental group (using the full explainable LAD) or a control group (using a visual-only LAD) to collaboratively write an academic abstract with a Generative AI. While quantitative analysis revealed no significant difference in the quality of co-authored abstracts between the two groups, a significant and noteworthy difference emerged in conceptual understanding: students in the explainable LAD group demonstrated a superior grasp of abstract writing principles, as evidenced by their higher scores on a knowledge test (p= .026). These findings highlight that while basic AI-generated feedback may suffice for immediate task completion, the provision of explainable feedback is crucial for fostering deeper learning, enhancing conceptual understanding, and developing transferable skills fundamental to self-regulated learning in academic writing contexts. |
| title | When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.16312 |