ORIGINAL RESEARCH article
Front. Educ.
Sec. Digital Learning Innovations
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1673956
This article is part of the Research TopicArtificial Intelligence in Educational Technology: Innovations, Impacts, and Future DirectionsView all 5 articles
Empowering College Students to Select Ideal Advisors: A Text-Based Recommendation Model
Provisionally accepted
- 1China University of Petroleum East China, Qingdao, China
- 2China University of Petroleum (East China), Qing Dao, China
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College students often encounter numerous challenges throughout their academic journeys, making the guidance and support from educators indispensable. Recommendation systems can significantly reduce the difficulties students face when identifying suitable academic advisors. This paper proposes a AdVisor RecommenDation (AVRD) model based on textual data regarding college students' interests. AVRD first adopts Chinese Bidirectional Encoder Representa-tions from Transformers (BERT) and unsupervised Simple Contrastive Learning of Sentence Embeddings (SimCSE) to train the corpus of advisors' records. The time decay factor is then introduced as the weight of the text record vectors, and the representation vectors of advisors are obtained using the weighted mean. Finally, the similarities between the advisor and student vectors are computed, and an advisor list is recommended to student according to the designed pool-ing and matching criteria. The questionnaire data from 170 college students are collected to evaluate the proposed model. Experimental results demonstrate the effectiveness of AVRD. The model outperforms other LLMs such as Qwen and DeepSeek by a significant margin, as well as the commonly used models like TF-IDF, LSA, and Word2Vec. Moreover, the ablation studies reveal that the SimCSE component of AVRD is crucial to the model's performance.
Keywords: advisor recommendation, text-based recommendation, bidirectionalencoder representations from transformers, simple contrastive learning of sentenceembeddings, Large Language Model
Received: 27 Jul 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Wang, Zhou, Jian, Yin and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ling Jian, bebetter@upc.edu.cn
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