Assessing the learning potential of freshmen in labor education courses using ordinal features and support vector machine

Introduction 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. Methods 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 cross-validation testing. Results Results indicate a remarkable overall accuracy rate of 97.75%, with an average sensitivity of 93.90% and an average specificity of 95.12%. Discussion The proposed method can play a role in enhancing teaching efficiency and tailoring interventions to individual students.