Mapping the Hotspots and New Trends of Online Teaching Evaluation: Evidence from Bibliometric Visualized Analysis

Online teaching has developed rapidly in the world, and online teaching evaluation (OTE)-related issues have attracted much attention. With the aid of bibliometric visualization software CiteSpace, a co-occurrence network analysis, clustering analysis, and co-citation analysis are developed based on the 1,285 Web of Science Core Collection database articles related to OTE within the period of 2000 to 2023. This paper attempts to find out the current research hotspots and development tendency in the field of OTE by adopting the method of keyword co-occurrence analysis and document co-citation analysis. Based on the above discussions, it has been figured out that in recent years, the quantity and attention degree of publication about OTE are both developing with a rapid-growth tendency, while the core cluster in authors is few and the institution collaboration is not close enough. Therefore, the research hotspots in OTE mainly involve seven aspects: formative evaluation of online teaching, machine learning, peer evaluation, learning satisfaction, MOOCs, evaluation factors, and online evaluation. The focus of frontier research on OTE lies in AI-based, machine learning-based systems, current studies on OTE in the COVID-19 context. It will help researchers understand the current status and development trends of OTE, hence further optimizing methods of evaluation for online teaching.