# LEARNet: A Learning Entropy-Aware Representation Network for Educational Video Understanding

## TL;DR

LEARNet is a new framework that efficiently identifies key instructional content in educational videos by focusing on high-information frames.

## Contribution

LEARNet introduces an entropy-aware architecture combining Temporal Information Bottleneck and Spatial–Semantic Decoder for educational video understanding.

## Key findings

- LEARNet reduces visual redundancy by 70.2% while maintaining high annotation accuracy (F1 = 0.89, mAP@50 = 0.88).
- The framework enables the creation of EVUD-2M, a large-scale benchmark with multi-level semantic labels for educational videos.

## Abstract

Educational videos contain long periods of visual redundancy, where only a few frames convey meaningful instructional information. Conventional video models, which are designed for dynamic scenes, often fail to capture these subtle pedagogical transitions. We introduce LEARNet, an entropy-aware framework that models educational video understanding as the extraction of high-information instructional content from low-entropy visual streams. LEARNet combines a Temporal Information Bottleneck (TIB) for selecting pedagogically significant keyframes with a Spatial–Semantic Decoder (SSD) that produces fine-grained annotations refined through a proposed Relational Consistency Verification Network (RCVN). This architecture enables the construction of EVUD-2M, a large-scale benchmark with multi-level semantic labels for diverse instructional formats. LEARNet achieves substantial redundancy reduction (70.2%) while maintaining high annotation fidelity (F1 = 0.89, mAP@50 = 0.88). Grounded in information-theoretic principles, LEARNet provides a scalable foundation for tasks such as lecture indexing, visual content summarization, and multimodal learning analytics.