LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910857243394048 |
|---|---|
| author | Davalos, Eduardo Zhang, Yike Srivastava, Namrata Salas, Jorge Alberto McFadden, Sara Cho, Sun-Joo Biswas, Gautam Goodwin, Amanda |
| author_facet | Davalos, Eduardo Zhang, Yike Srivastava, Namrata Salas, Jorge Alberto McFadden, Sara Cho, Sun-Joo Biswas, Gautam Goodwin, Amanda |
| contents | Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching standards -- to derive meaningful reading insights. We employ unsupervised learning techniques to identify distinct reading behavior patterns, and then a large language model (LLM) synthesizes the derived information into actionable reports for educators, streamlining the interpretation process. LLM experts and human educators evaluate these reports for clarity, accuracy, relevance, and pedagogical usefulness. Our findings indicate that LLMs can effectively function as educational analysts, turning diverse data into teacher-friendly insights that are well-received by educators. While promising for automating insight generation, human oversight remains crucial to ensure reliability and fairness. This research advances human-centered AI in education, connecting data-driven analytics with practical classroom applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports Davalos, Eduardo Zhang, Yike Srivastava, Namrata Salas, Jorge Alberto McFadden, Sara Cho, Sun-Joo Biswas, Gautam Goodwin, Amanda Computers and Society Artificial Intelligence Human-Computer Interaction I.2.1; I.2.7; K.3.1 Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching standards -- to derive meaningful reading insights. We employ unsupervised learning techniques to identify distinct reading behavior patterns, and then a large language model (LLM) synthesizes the derived information into actionable reports for educators, streamlining the interpretation process. LLM experts and human educators evaluate these reports for clarity, accuracy, relevance, and pedagogical usefulness. Our findings indicate that LLMs can effectively function as educational analysts, turning diverse data into teacher-friendly insights that are well-received by educators. While promising for automating insight generation, human oversight remains crucial to ensure reliability and fairness. This research advances human-centered AI in education, connecting data-driven analytics with practical classroom applications. |
| title | LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction I.2.1; I.2.7; K.3.1 |
| url | https://arxiv.org/abs/2503.02099 |