Abstract
Along with the big data era, digital transformation has had a transformative effect on modern education tremendously in higher education. It transforms an institutional core value of education to better meet students' needs by leveraging big data and digital technology. Based on this background, this study attempts to catch the principal trends, or new directions, paradigms as predictors with an association of each topic by discovering the up-to-date research trends on teaching and learning in higher education via text mining techniques. For this, 285 research articles in the area of teaching and learning in higher education were collected from several big databases (distinguishable publishers' web platforms) through search engines for 2 years in 2018-2019. Then it was analyzed using a semantic network analysis that processes natural human language. Consequently, research results show a relatively high connection with 'student' or 'student-centered/led' rather than 'teacher-led.' Moreover, it exhibits that the practice and assessment in learning can be attained via diverse learning activities, containing community or outreach activities. Besides, research in academic contexts, experience-based classes, the effect of group activities, how students' feelings or perceptions, and relationships affect learning outcomes were addressed as the main topics through topic modeling of LDA, a machine learning algorithm. This study proposes that educators, researchers, and even academic leaders can exert extraordinary power to reshape educational quality programs for future education and in a timely manner with recognizable trends or agendas in teaching and learning of higher education.