ORIGINAL RESEARCH article
Front. Educ.
Sec. Higher Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1483964
Assessing the Learning Potential of Freshmen in Labor Education Courses Using Ordinal Features and Support Vector Machine
Provisionally accepted
Yan Yang*
Long Yan- Jinzhou Medical University, Jinzhou, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Artificial intelligence (AI) marks a new wave of the information technology revolution and permeates various sectors as an indispensable tool. Despite its widespread adoption, its application in enhancing college students' labor education remains scantily explored. Conventional teaching approaches often fail to assess students' foundational knowledge accurately, impeding personalized learning. Hence, the current environment underscores the pressing necessity for a robust AI framework capable of reliably predicting individual students' learning aptitude.In this study we constructed a multidimensional feature vector model, leveraging data on students' academic performance during their middle school years and their willingness to participate in college-level labor education. Through the usage of Support Vector Machines (SVM), we aim to assess students' learning potential effectively. To validate the efficacy of our predictive model, we conducted jackknife crossvalidation testing. Results indicate a remarkable overall accuracy rate of 97.75%, with an average sensitivity of 93.90% and an average specificity of 95.12%.
Keywords: AI, college labor, Feature vector, SVM - Support vector machine, k-mer
Received: 21 Aug 2024; Accepted: 05 Aug 2025.
Copyright: © 2025 Yang and Yan. 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: Yan Yang, Jinzhou Medical University, Jinzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.