An Efficient Processing Model for Improving Technology Teaching
Shiqing Chen
International Journal of Computational Linguistics Research, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/ijclr/2025/16/4/146-154
Link: https://www.dline.info/jcl/fulltext/v16n4/jclv16n4_2.pdf
Abstract:
The paper proposes a practical teaching platform for engineering students based on a tag based talent search algorithm, aiming to bridge the gap between theoretical education and real world engineering demands. Traditional teaching methods often neglect hands on experience, limiting studentsâ€™ problem solving and teamwork abilities. To address this, the authors design a system that matches students with suitable practical projects by aligning student tags (e.g., skills, interests, experience) with project tags (e.g., domain, complexity, type). The algorithm leverages techniques like TF-IDF, binary encoding, and classification models (e.g., Naive Bayes, neural networks) to enable precise matching. The study outlines steps including tag definition, feature selection, dataset construction, algorithm training, and performance evaluation using metrics like accuracy, recall, and F1 score. Experimental results suggest that increased historical data improves recommendation accuracy, though the tag based approach shows comparable though not always superior performance against other algorithms in large data scenarios. The platform enhances studentsâ€™ practical competencies and employability while supporting applied talent cultivation models in engineering education. However, the research acknowledges limitations, such as a small dataset and the need for broader algorithm comparisons. Future work includes scaling the dataset and exploring alternative matching algorithms to improve generalization and effectiveness. The paper contributes to the growing field of intelligent educational systems by integrating talent matching algorithms in to engineering pedagogy.