Statistical Academic Evaluation of the Teaching Process in Engineering Education

Statistics is a valuable and reliable tool for extracting meaningful insights from raw data and has wide applicability across disciplines. This study demonstrates the effective application of statistical methods in evaluating the teaching process within the Engineering Science department at the Libyan International University.  Fundamental statistical tools—mean, standard deviation, and analysis of variance (ANOVA)—were employed to analyze student performance across various courses, revealing that the instructional process remains consistent and under control, with no significant differences observed between courses. Additionally, multiple linear regression was utilized to predict students’ grade point averages based on key instructional variables: number of weeks per course, weekly teaching hours, and number of lectures per course. To complement quantitative data, a comprehensive satisfaction questionnaire was administered to both students and faculty members two weeks prior to final exams. Student feedback addressed the quality of content presentation, teaching and learning methods, evaluation strategies, and faculty interaction. Faculty responses focused on the adequacy of learning resources, instructional strategies, assessment quality, and student engagement. Results affirmed overall satisfaction and alignment between teaching practices and student expectations. This integrative approach underscores the utility of combining statistical analysis with perception-based assessments to holistically evaluate and improve educational quality.