Data-Driven Analysis of Digital Mentoring Models Using Cronbach’s Alpha and ANOVA in Higher Education Faculty Development
摘要
In higher education, mentoring instructors has become increasingly important as a way of keeping employees on board, encouraging their professional growth, and ensuring their continued presence on the staff. However, because there is insufficient empirical data, researchers are unable to assess the effectiveness of different mentoring approaches, such as formal, peer-based, informal and hybrid. This study investigated the impact of different mentorship approaches on faculty job satisfaction, research output and intention to stay at their current university using quantitative indicators. To assist with the research, an email containing a Likert-type questionnaire with preset items was sent to 200 faculty members from various institutions. The reliability of the tool was demonstrated by its Cronbach’s alpha of 0.84. According to a one-way analysis of variance, research productivity and faculty job satisfaction varied significantly (p < .05) between different mentoring approaches. Compared to formal and hybrid mentoring approaches, peer-based mentoring was found to be the most successful strategy for improving faculty outcomes. While there was no statistically significant difference in teacher retention amongst the different mentoring methodologies, it is evident that other organisational factors may influence the faculty retention rate, necessitating further research. For academic leaders and decision-makers looking to improve faculty support through evidence-based mentorship programmes, the study’s findings provide crucial new information. The study examines the effects of mentoring on faculty job satisfaction, productivity, and retention to provide insights into the creation of organised, encouraging mentoring programmes that promote faculty development and maximise institutional goals for academic accomplishment and faculty well-being.