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.

Purpose: This study examined the influence of metacognitive awareness, epistemological beliefs, and mathematical resilience on university students’ perceived mathematical problem-solving ability and explored the cognitive and affective processes students employ when solving mathematical problems. Design/methodology/approach: An explanatory mixed-methods design was employed. Quantitative data were collected from 350 undergraduate Mathematics Education students at a public university in Ghana using a structured questionnaire and analyzed using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. Qualitative data were obtained from 10 purposively selected students and analysed thematically to provide further insight into students’ problem-solving processes. Findings: Metacognitive awareness (β = .247, p < .001) and epistemological beliefs (β = .273, p < .001) significantly predicted perceived mathematical problem-solving ability, whereas mathematical resilience did not significantly predict perceived mathematical problem-solving ability (β = .105, p = .163). The qualitative findings indicated that students engaged in problem interpretation and strategy planning, monitoring and evaluation, persistence and strategy adjustment, and experienced varied emotional responses during problem solving. Practical implications: Mathematics instruction should incorporate activities that strengthen students’ metacognitive regulation and productive epistemological beliefs while developing resilience alongside mathematical knowledge and problem-solving strategies. Originality/value: The study combines structural evidence on cognitive and epistemological predictors of mathematical problem-solving with qualitative accounts of students’ cognitive and affective experiences, providing a more comprehensive account of problem-solving among university mathematics education students.

Binary classifications of teaching as transmission or constructivism obscure how participation, authority, knowledge practices, and conceptual work can vary within one lesson. This article introduces the social, procedural, epistemic, and conceptual (SPEC) framework for episode-level lesson analysis. SPEC was developed through a structured purposive theory synthesis rather than an exhaustive systematic review. A documented corpus of foundational traditions, observation systems, and targeted verification sources was compared using explicit inclusion, boundary, and disposition rules. Candidate constructs were assigned by observable target; three indicators per dimension were retained as a parsimonious authorial proposal; and qualitative descriptors were generated from theoretically warranted adjacent distinctions checked against cross-dimensional counterexamples. The dimensions are analytically distinguishable but potentially related. Their descriptor codes form non-additive, non-normative profiles rather than composite scores or judgments of teaching quality. An instructional episode is the primary unit; a hypothetical transcript illustrates indicator coding, opportunity/evidence flags, and rejection of nearby alternatives. SPEC is an initial conceptual proposal requiring operationalization, reliability examination, and validity testing. Ordering, thresholds, and cross-domain use require study before evaluative application.