LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning
This addresses the problem of scalable and accurate evaluation for educators and content creators in multimedia learning, though it is incremental as it builds on existing theories and datasets.
The paper tackles the challenge of evaluating slide-based multimedia instruction by introducing LecEval, an automated metric based on Mayer's Cognitive Theory of Multimedia Learning, which assesses effectiveness using four rubrics and shows superior accuracy on a dataset of over 2,000 slides.
Evaluating the quality of slide-based multimedia instruction is challenging. Existing methods like manual assessment, reference-based metrics, and large language model evaluators face limitations in scalability, context capture, or bias. In this paper, we introduce LecEval, an automated metric grounded in Mayer's Cognitive Theory of Multimedia Learning, to evaluate multimodal knowledge acquisition in slide-based learning. LecEval assesses effectiveness using four rubrics: Content Relevance (CR), Expressive Clarity (EC), Logical Structure (LS), and Audience Engagement (AE). We curate a large-scale dataset of over 2,000 slides from more than 50 online course videos, annotated with fine-grained human ratings across these rubrics. A model trained on this dataset demonstrates superior accuracy and adaptability compared to existing metrics, bridging the gap between automated and human assessments. We release our dataset and toolkits at https://github.com/JoylimJY/LecEval.