An Open Workflow Model for Improving Educational Video Design: Tools, Data, and Insights

TL;DR

This paper introduces a comprehensive, open-source workflow and dataset for analyzing and improving educational video design, supported by machine learning insights into factors affecting student engagement.

Contribution

It presents a novel open workflow, tools, and dataset for data-driven educational video design, along with a case study and initial analysis of engagement factors.

Findings

Identified key video features influencing engagement

Developed open-source tools for video metadata extraction

Provided a community-driven database of educational videos

Abstract

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.