In the current study we examined the relationships between student evaluations of lecturers (teaching surveys) and faculty members' perceptions of these surveys as capable of blocking and limiting their professional advancement. Faculty members are judged and evaluated by academic authorities for their academic performance in research and teaching. 178 questionnaires were collected from the faculty of several academic institutions. We employ a mix method analysis, and form a model that reflects the factors perceived by faculty members as having the potential to block their professional advancement in academia. The research findings show that lecturers are of the opinion that teaching load has a detrimental effect on students' evaluations in the surveys. Lecturers at the beginning of their academic life, those in lower ranks: senior teacher and senior lecturer, address the negative aspects of the surveys more than others. The research findings indicate that although more hours are taught in colleges than at universities, it is harder to receive positive survey ratings at colleges. Moreover, since in Israeli academia research is still the main criterion for promotion – faculty members born in Israel were found to teaching less than those born elsewhere. Hence, faculty members think that student surveys are destructive and entail risks for their professional advancement. Assuming that students' voice and opinions on teaching are important – how can a balance be achieved between the research achievements of faculty members and student satisfaction?

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.