Hotspots and Visual Analysis of Academic Early Warning Research for College Students
Year: 2025
Type: article
Abstract: This study used China National Knowledge Infrastructure (CNKI) as the data source and selected 385 research papers on academic warning in universities published between 2005 and 2024 for analysis. The CiteSpace analysis tool was used to visualize the literature, and the results showed that research on academic warning in China began in 2007 and has gone through stages of initial exploration, diversified explosion, and innovation deepening; The research is mainly focused on education and psychology in terms of disciplinary distribution, with computer science as a supplement; Most researchers tend to conduct research independently, with limited collaborative power and only a few small-scale collaborative teams present, resulting in relatively insufficient cooperation between different institutions; The research hotspots focus on academic difficulties, academic crises, and academic assistance, and in recent years, there has been a gradual shift towards using big data and intelligent algorithms for research; The academic warning for college students is mainly clustered into eight core topics: credit system, warning mechanism, assistance mechanism, data mining, student management, countermeasures, students with learning difficulties, and delayed graduation. The research results suggest that future research can expand the scope of disciplines, strengthen cooperation between authors and institutions, discover students with potential academic crises through mining data information and applying artificial intelligence algorithms, improve warning and assistance mechanisms, timely formulate precise tutoring strategies to support and assist students, improve their learning quality, and successfully complete academic tasks. These will become important hot topics of concern for major universities and researchers in the future.
DOI: 10.61784/ejst3080
Authors: XiaoPeng Tang, YunLiang Jiang, HaiLong Sun
Institution: Civil Aviation University of China
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4409949994