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.

随着大数据时代的到来，数字化转型已对高等教育中的现代教育产生了巨大的变革性影响。它利用大数据和数字技术，转变了教育的机构核心价值，以更好地满足学生的需求。在此背景下，本研究试图通过文本挖掘技术发现有关高等教育教学的最新研究趋势，以捕捉与各主题相关的主要趋势或新方向，范式作为预测变量。为此，在2018-2019年期间，通过搜索引擎从多个大型数据库（可区别出版商的网络平台）中收集了285篇有关高等教育教学领域的研究文章，为期2年。然后使用处理自然人类语言的语义网络分析对其进行分析。因此，研究结果表明与“学生”或“以学生为中心/领导”而不是“以老师为主导”的联系相对较高。而且，它表明，可以通过包括社区活动或外展活动在内的各种学习活动来实现对学习的实践和评估。此外，通过机器学习算法LDA的主题建模，研究了学术环境，基于经验的课程，小组活动的效果，学生的感受或看法以及关系如何影响学习成果的研究，并将其作为主要主题。这项研究建议教育者，研究人员，