Gurcan, FatihÇağıltay, NergizErdogdu, FatihCagiltay, Nergiz ErcilCagiltay, KursatSoftware Engineering2024-07-052024-07-05202321360-23571573-760810.1007/s10639-023-11803-82-s2.0-85153395466https://doi.org/10.1007/s10639-023-11803-8https://hdl.handle.net/20.500.14411/2542GURCAN, Fatih/0000-0001-9915-6686Student engagement is critical for both academic achievement and learner satisfaction because it promotes successful learning outcomes. Despite its importance in various learning environments, research into the trends and themes of student engagement is scarce. In this regard, topic modeling, a machine learning technique, allows for the analysis of large amounts of content in any field. Thus, topic modeling provides a systematic methodology for identifying research themes, trends, and application areas in a comprehensive framework. In the literature, there is a lack of topic modeling-based studies that analyze the holistic landscape of student engagement research. Such research is important for identifying wide-ranging topics and trends in the field and guiding researchers and educators. Therefore, this study aimed to analyze student engagement research using a topic modeling approach and to reveal research interests and trends with their temporal development, thereby addressing a lack of research in this area. To this end, this study analyzed 42,517 peer-reviewed journal articles published from 2010 to 2019 using machine learning techniques. According to our findings, two new dimensions, "Community Engagement" and "School Engagement", were identified in addition to the existing ones. It is also envisaged that the next period of research and applications in student engagement will focus on the motivation-oriented tools and methods, dimensions of student engagement, such as social and behavioral engagement, and specific learning contexts such as English as a Foreign Language "EFL" and Science, Technology, Engineering and Math "STEM".eninfo:eu-repo/semantics/closedAccessStudent engagementTopic modelingText miningTrend analysisMachine learningArticleQ1Q128111506715091WOS:000975863900001