This study applies explainable machine learning to identify which vocal attributes in a lecturer’s speech influence students’ views of a lecturer’s charisma, a key contributor to teaching quality. It further explores whether vocal qualities differ between male and female lecturers and how students of different genders respond to these differences, offering insights into voice-related factors that influence the impact of educators. Speech segments from YouTube videos featuring 200 native-English lecturers were evaluated by 900 students using charisma rating scales. A set of attributes related to three primary prosodic dimensions of voice - pitch, rhythm, and loudness - was computed. A random forest classifier was employed to predict the charisma level based on the speech attributes and to list and rank the attributes that contributed most to the prediction. The findings revealed prominent vocal attributes that achieved higher charisma scores in the students' ratings. Same-gender evaluations of charisma were mainly based on pitch, while cross-gender evaluations rely mostly on loudness or rhythm. The automated, interpretable method provides a reliable and efficient way to measure vocal charisma in academic lecturers. It can be adapted to examine additional individual factors that influence the perception of a lecturer’s charismatic presence. It may also be integrated into practice-based tools, designed to support instructors in improving their presentation skills. Our research bridges the fields of applied psychology and computer science to contribute to the development of educational technology.

Within the context of investigating belief systems, this work exemplifies a mixed-method approach. Two approaches are utilized to explore the philosophical, ontological, and epistemological assumptions that university students foster regarding the relationships between knowledge and reality. In the first step, written materials that elaborated on the matter at hand were subjected to content analysis with the assistance of Leximancer, a software that recognizes themes and concepts and turns textual data into concept maps that express networks of meaning. The second step involved conducting a cluster analysis on the data obtained from the questionnaire to identify distinct groups of participants who shared consistent epistemological viewpoints. The results obtained from the two approaches are in agreement and shed light on the prevalent epistemic inclination that favors a constructivist viewpoint. Discussion on the ramifications of the findings, as well as the methodological issues that are pertinent to the present illustration, is provided.

Team-based learning (TBL) is a popular form of collaborative learning designed to increase student engagement and motivate students to learn. A growing body of research, particularly in the health sciences, has demonstrated that TBL has positive impacts on student performance and classroom dynamics. However, much less is known about the student outcomes associated with TBL courses in undergraduate science, technology, engineering, and mathematics (STEM) education, fields in which active learning is especially important for student success and retention. To address this gap, we conducted a systematic review of the student outcomes associated with TBL in undergraduate STEM education. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we identified 55 empirical and qualitative research articles published between 2005 and 2024 that reported on TBL implementation practices and student outcomes. Importantly, we found that most studies described increased student performance and improved measures of classroom dynamics when TBL was compared to lecture-based teaching approaches. These findings provide further evidence that TBL is an effective instructional method and suggest that TBL can be implemented successfully across a wide range of student populations and undergraduate STEM disciplines.