Predicting Student's Final Examination Grades in Statistics Courses Using a Regression Model Based on Internal Assessment Scores

Learning analytics is the process of collecting data on learners and their environment, analyzing it, and using those insights to improve learning experiences and outcomes. Analysis will help teachers identify students who might need extra help early on. Also, courses can be tailored to individual learning styles and the entire learning environment can be fine-tuned for better results. A multiple linear regression model is presented here in this work to predict students' Final Exam grades from internal assessment scores. In this case, the response variable is the student's score in the Final Exam, while the assessment components, such as Quiz, Midterm, and SCL, are predictor variables. The data were collected from a group of students who studied Probability Statistics course at the advanced diploma level program. A regression model was created using R software in this study, accompanied by graphical representations and tables to demonstrate the model.