Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2512.16254 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917153649721344 |
|---|---|
| author | Tolba, Mohamed Kendall, Olivia Smith, Daniel Tudball Gregg, Alexander Vo, Tony Wordley, Scott |
| author_facet | Tolba, Mohamed Kendall, Olivia Smith, Daniel Tudball Gregg, Alexander Vo, Tony Wordley, Scott |
| contents | Educational videos are widely used across various instructional models in higher education to support flexible and self-paced learning. However, student engagement with these videos varies significantly depending on how they are designed. While several studies have identified potential influencing factors, there remains a lack of scalable tools and open datasets to support large-scale, data-driven improvements in video design. This study aims to advance data-driven approaches to educational video design. Its core contributions include: (1) a workflow model for analysing educational videos; (2) an open-source implementation for extracting video metadata and features; (3) an accessible, community-driven database of video attributes; (4) a case study applying the approach to two engineering courses; and (5) an initial machine learning-based analysis to explore the relative influence of various video characteristics on student engagement. This work lays the groundwork for a shared, evidence-based approach to educational video design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Open Workflow Model for Improving Educational Video Design: Tools, Data, and Insights Tolba, Mohamed Kendall, Olivia Smith, Daniel Tudball Gregg, Alexander Vo, Tony Wordley, Scott Applications Physics Education Educational videos are widely used across various instructional models in higher education to support flexible and self-paced learning. However, student engagement with these videos varies significantly depending on how they are designed. While several studies have identified potential influencing factors, there remains a lack of scalable tools and open datasets to support large-scale, data-driven improvements in video design. This study aims to advance data-driven approaches to educational video design. Its core contributions include: (1) a workflow model for analysing educational videos; (2) an open-source implementation for extracting video metadata and features; (3) an accessible, community-driven database of video attributes; (4) a case study applying the approach to two engineering courses; and (5) an initial machine learning-based analysis to explore the relative influence of various video characteristics on student engagement. This work lays the groundwork for a shared, evidence-based approach to educational video design. |
| title | An Open Workflow Model for Improving Educational Video Design: Tools, Data, and Insights |
| topic | Applications Physics Education |
| url | https://arxiv.org/abs/2512.16254 |