Assessing Course Outcomes and Predicting Grades in an Undergraduate Level Program: Insights from Performance Analysis and Factor Regression Modeling

As part of an educational program, course outcome assessments play a significant role in evaluating students’ understanding of the course content they are learning and their ability to apply it in a real time situation. The quality of these assessments also influences student motivation, which affects their engagement in the classroom as well as their ability to learn effectively. Students can become active, engaged, and motivated learners when assessments are meaningful, aligned with learning objectives, and supportive of student growth. As a key indicator of quality in education, this research aims to evaluate the effectiveness of the courses in achieving the desired learning outcomes. This study employs an exploratory data analysis (EDA) to analyze the intricate relationships among variables and their impact on student performance. We use descriptive statistics to summarize the data and to give a clear picture of the achievement levels for the different course outcomes based on the data. In addition, factor analysis and regression modeling are employed in this study to analyze the relationship between course outcomes and grade predictions. We employ factor analysis to capture the essence of each course outcome to extract the most information from many variables. The results indicate variations in the proficiency levels among students in the course outcomes. Most of the course outcomes showed positive correlations, indicating that the higher performance in one outcome was consistently connected with a higher performance in other outcomes. In addition, the regression model developed predicts the grades with a reasonable degree of accuracy. The findings of this study will provide invaluable insights for educators and administrators seeking to better understand student performance and optimize educational outcomes for their students.

Keywords: Course outcome; Outcome assessment; Factor analysis, Regression model