Towards teaching analytics: a contextual model for analysis of students' evaluation of teaching through text mining and machine learning classification.

Recent trends in have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching-learning processes. Didactically, is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching-learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers' performances and recommendations based on data derived from students' evaluation of teaching (SET).

The impact of the COVID-19 pandemic on students' feelings at high school, undergraduate, and postgraduate levels.

The COVID-19 pandemic and the enforced restrictions have harshly affected educational sectors in 161 countries around the world. With more than 1.6 billion students away from normal school life, the crisis threatens the teaching and learning processes and the students' emotional health.

Does professors' gender impact how students evaluate their teaching and the recommendations for the best professor?

This study examined the impact of the professors' gender according to a student evaluation of teaching (SET) in a private university. The study took place in a private university (n = 103,833) on six different campuses in the north region of Mexico. The distribution of the professors' gender was analyzed according to semesters, campuses, and schools.