There are studies in the learning management literature examining the measure of system usage, but few explore how users apply the software tools to achieve specific work tasks, which in turn leads to perceived benefits. In the context of distance education, this study focuses on how Learning Management Systems (LMS) are fully used by faculty for their instructional needs. It extends existing research on LMS adoption by investigating how faculty members or instructors use the LMS tools for effective class teaching to achieve educational outcomes. Four usage patterns were identified: communication, content management, assessment, and class management. A model is presented to examine how these usage patterns interplay to achieve the perceived benefits. Data were collected from 544 instructors using LMS, such as Blackboard Learn, etc. Structural equation modeling using LISREL was employed to assess the research model. The results suggest that the usage for communication, content, and assessment activities positively impacts the usage for class management. In turn, the usage for class management influences the net benefits perceived by the instructors, and the usage for content also impacts perceived net benefits directly. These results provide practical guidelines for LMS developers’ design improvements and institutions’ policies, such as training instructors to fully utilize LMS features to achieve the maximum benefits of distance education.

Self-report surveys are extensively utilized in educational research to understand students’ perceptions and experiences. However, younger children, particularly those in elementary school, may exhibit a tendency to provide socially desirable responses, potentially compromising the data quality. This study examined the prevalence and impact of socially desirable responses in self-report surveys administered to elementary school students. A total of 1,024 students from grades 4 and 5 in five elementary schools participated in the study. Socially desirable responses were measured using detection items embedded within questionnaires. The findings indicate that (a) more than 20% of elementary school students demonstrated socially desirable responses; (b) female students and those with higher academic achievement were more likely to provide socially desirable responses; (c) socially desirable responses skewed the sample distribution by inflating mean scores and reducing standard deviations; and (d) while internal correlations within scales remained relatively stable, external validity, as reflected in correlations between self-reports and academic performance metrics, was significantly affected after adjusting for socially desirable responses. These results underscore the importance of addressing socially desirable responses when interpreting self-report data from young students. The study concludes with practical recommendations for improving the validity of self-report surveys in educational research.

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.