Course Recommender Model.pdf

This study proposes a tool, DALAN, that uses multiple regression to forecast incoming college students' courses at Notre Dame of Midsayap College. The model is based on Entrance Exam results, High School Grade Point Average, and Cumulative Semestral Grade Point Average of College of Information Technology and Engineering students. The study found that Entrance Exam results and High School Grade Point Average significantly impact Cumulative Semestral Grade Point Average.

Key Findings:

The developed predictive model is suitable for course recommendation.
Entrance Exam results and High School Grade Point Average are significant predictors of Cumulative Semestral Grade Point Average.
The model can help students choose suitable courses and plan for academic periods.

Methodology:

Data was collected from the College of Information Technology and Engineering department.
Multiple Regression analysis was used to produce results and recommend courses.
The study used Pearson's statistical theory of multiple regression.

Conceptual Framework:

The study uses the Input-Process-Output (IPO) model.
Training data consists of High School Grade Point Average and Entrance Exam Result.
The data is processed using DALAN, which uses a Multiple Regression Model to develop a predictive model.
The output contains the College Semestral Grade Point Average, which is the result of the analyzed data.

Related Works:

Educational Data Mining (EDM) is used to predict courses that can help students choose suitable courses.
Student Performance Prediction (SPP) aims to evaluate the grade a student will reach before taking an exam or enrolling in a course.
* Recommender systems are essential for a student's future success, considering different factors to identify and deal with uncertainty.