Open Access Peer-reviewed Research Article

Automated Video Title Generation for Mobile Learning Resources: A Deep Learning Approach with Educational Context Awareness

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Key Frame Extraction Process
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Submitted   Dec 3, 2024
Published   Mar 13, 2025
Issue    Vol 5 No 1 (2025)
DOI   10.25082/AMLER.2025.01.010

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Zheng Gong corresponding author
[1] Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China; [2] School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China

Abstract

With the rapid growth of mobile learning platforms, short educational videos have emerged as a critical resource for learners. However, manually generating concise and pedagogically meaningful titles for these videos remains a time-consuming challenge. To address this issue, this study proposes a deep learning framework designed for automated video title generation in educational contexts. The framework integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and natural language processing (NLP) techniques, with explicit awareness of pedagogical relevance. The proposed approach operates in three stages: 1) extracting key frames from input videos using an optimized shot detection algorithm, 2) analyzing these frames with CNN models to derive semantic representations of visual content, and 3) processing the representations through an LSTM network to generate descriptive text. The output is further refined using the TextRank algorithm to ensure conciseness and contextual coherence. Experimental results demonstrate that our framework effectively generates high-quality video titles that are both educationally informative and contextually engaging, outperforming baseline methods in alignment with curriculum standards and learner-centric search intent.

Keywords
mobile learning, deep learning, convolutional neural networks, natural language processing, video captioning

Article Details

How to Cite
Gong, Z. (2025). Automated Video Title Generation for Mobile Learning Resources: A Deep Learning Approach with Educational Context Awareness. Advances in Mobile Learning Educational Research, 5(1), 1344-1355. https://doi.org/10.25082/AMLER.2025.01.010
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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