Dynamic Tracking and Guidance Strategies for Academic Stress in Vocational College Students Based on Big Data Learning Analysis
Year: 2026
Type: conference-paper
Abstract: This article applies big data learning analytics to investigate academic stress among vocational college students. Given the skill-oriented training goals and practical curriculum in vocational education, students’ academic stress is complex and dynamically evolving, making traditional static surveys inadequate for real-time capture and personalized intervention. To address this, the study constructs a framework comprising data collection, stress modeling, dynamic tracking, and guided intervention. A multi-source data module collects learning behaviors, grades, attendance, and psychological evaluations; a machine learning model predicts academic stress for dynamic tracking; and a tiered guidance strategy is implemented based on tracking results. A one-semester experiment at a vocational college shows that the consistency between model-identified high-stress students and self-reported feedback exceeds 90%, validating the model's capability for real-time stress detection and revealing distinct stress patterns across majors, thereby providing a precise basis for targeted interventions.
Authors: Rui Lang, Yanrong Cheng, Suying Liu, Yang Zhang
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7208749086