PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918321363877888 |
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| author | Ozaki, Mahiro Chen, Li Naganuma, Shotaro Švábenský, Valdemar Okubo, Fumiya Shimada, Atsushi |
| author_facet | Ozaki, Mahiro Chen, Li Naganuma, Shotaro Švábenský, Valdemar Okubo, Fumiya Shimada, Atsushi |
| contents | This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curriculum-level information. Traditional LA research has predominantly focused on individual courses or learners and often lacks a framework that considers the relationships between courses and the long-term trajectory of learning. To bridge this gap, PALM was developed to integrate multilayered educational data into a curriculum map, enabling learners to intuitively understand their learning records and academic progression. We conducted a system evaluation to assess PALM's effectiveness in two key areas: (1) its impact on students' awareness of their learning behaviors, and (2) its comparative performance against existing systems. The results indicate that PALM enhances learners' awareness of study planning and reflection, particularly by improving perceived behavioral control through the visual presentation of individual learning histories and statistical trends, which clarify the links between learning actions and outcomes. Although PALM requires ongoing refinement as a system, it received significantly higher evaluations than existing systems in terms of visual appeal and usability. By serving as an information resource with previously inaccessible insights, PALM enhances self-regulated learning and engagement, representing a significant step beyond conventional LA toward a comprehensive and scalable approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18393 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses Ozaki, Mahiro Chen, Li Naganuma, Shotaro Švábenský, Valdemar Okubo, Fumiya Shimada, Atsushi Human-Computer Interaction Computers and Society This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curriculum-level information. Traditional LA research has predominantly focused on individual courses or learners and often lacks a framework that considers the relationships between courses and the long-term trajectory of learning. To bridge this gap, PALM was developed to integrate multilayered educational data into a curriculum map, enabling learners to intuitively understand their learning records and academic progression. We conducted a system evaluation to assess PALM's effectiveness in two key areas: (1) its impact on students' awareness of their learning behaviors, and (2) its comparative performance against existing systems. The results indicate that PALM enhances learners' awareness of study planning and reflection, particularly by improving perceived behavioral control through the visual presentation of individual learning histories and statistical trends, which clarify the links between learning actions and outcomes. Although PALM requires ongoing refinement as a system, it received significantly higher evaluations than existing systems in terms of visual appeal and usability. By serving as an information resource with previously inaccessible insights, PALM enhances self-regulated learning and engagement, representing a significant step beyond conventional LA toward a comprehensive and scalable approach. |
| title | PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses |
| topic | Human-Computer Interaction Computers and Society |
| url | https://arxiv.org/abs/2507.18393 |