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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2505.12064 |
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| _version_ | 1866915291723726848 |
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| author | Sadallah, Madjid |
| author_facet | Sadallah, Madjid |
| contents | Learning Analytics Dashboards (LADs) often fall short of their potential to empower learners, frequently prioritizing data visualization over the cognitive processes crucial for translating data into actionable learning strategies. This represents a significant gap in the field: while much research has focused on data collection and presentation, there is a lack of comprehensive models for how LADs can actively support learners' sensemaking and self-regulation. This paper introduces the Adaptive Understanding Framework (AUF), a novel conceptual model for learner-centered LAD design. The AUF seeks to address this limitation by integrating a multi-dimensional model of situational awareness, dynamic sensemaking strategies, adaptive mechanisms, and metacognitive support. This transforms LADs into dynamic learning partners that actively scaffold learners' sensemaking. Unlike existing frameworks that tend to treat these aspects in isolation, the AUF emphasizes their dynamic and intertwined relationships, creating a personalized and adaptive learning ecosystem that responds to individual needs and evolving understanding. The paper details the AUF's core principles, key components, and suggests a research agenda for future empirical validation. By fostering a deeper, more actionable understanding of learning data, AUF-inspired LADs have the potential to promote more effective, equitable, and engaging learning experiences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12064 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Data to Actionable Understanding: A Learner-Centered Framework for Dynamic Learning Analytics Sadallah, Madjid Human-Computer Interaction Learning Analytics Dashboards (LADs) often fall short of their potential to empower learners, frequently prioritizing data visualization over the cognitive processes crucial for translating data into actionable learning strategies. This represents a significant gap in the field: while much research has focused on data collection and presentation, there is a lack of comprehensive models for how LADs can actively support learners' sensemaking and self-regulation. This paper introduces the Adaptive Understanding Framework (AUF), a novel conceptual model for learner-centered LAD design. The AUF seeks to address this limitation by integrating a multi-dimensional model of situational awareness, dynamic sensemaking strategies, adaptive mechanisms, and metacognitive support. This transforms LADs into dynamic learning partners that actively scaffold learners' sensemaking. Unlike existing frameworks that tend to treat these aspects in isolation, the AUF emphasizes their dynamic and intertwined relationships, creating a personalized and adaptive learning ecosystem that responds to individual needs and evolving understanding. The paper details the AUF's core principles, key components, and suggests a research agenda for future empirical validation. By fostering a deeper, more actionable understanding of learning data, AUF-inspired LADs have the potential to promote more effective, equitable, and engaging learning experiences. |
| title | From Data to Actionable Understanding: A Learner-Centered Framework for Dynamic Learning Analytics |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.12064 |