Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 11 Dec 2023 English Publisher:PeerJJournal:PeerJ Computer Science, volume 9, page e1689 (eissn: 2376-5992,

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Authors: Carlos Giovanny Hidalgo Suarez; Javier Llanos; Víctor Bucheli;

doi: 10.7717/peerj-cs.1689 , 10.5281/zenodo.8209973 , 10.60692/t69t9-d7v97 , 10.5281/zenodo.8209972 , 10.60692/q68ce-vrw06

pmid: 38192444

pmc: PMC10773829

Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses

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Abstract

This article introduces a model for accurately predicting students’ final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared (R2) value ranging from 72% to 91%. The discussion section focuses on four crucial aspects: the selection of data features and the percentage of course grades used for training, the comparison between predicted and actual values to demonstrate reliability, and the model’s performance compared to existing literature models, highlighting its effectiveness.

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Universidad de San Buenaventura, Bogota
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Keywords

FOS: Computer and information sciences, Artificial intelligence, Support vector machine, Predictive Analysis, Regression model, Course grade, Adaptive Learning, Model selection, Quantum mechanics, Predicting final grade, Learning with Noisy Labels in Machine Learning, Student Performance Evaluation, Model Prediction, Engineering, Artificial Intelligence, Machine learning, FOS: Mathematics, Physics, AdaBoost, Statistics, Application of Fuzzy Logic in Educational Assessment, QA75.5-76.95, Power (physics), Computer science, Regression, Computer Science Applications, Reliability (semiconductor), Aerospace engineering, Electronic computers. Computer science, Computer Science, Physical Sciences, Course (navigation), Student Performance Prediction, Educational Data Mining and Learning Analytics, CS1, Regression analysis, Mathematics, Information Systems, Robust Learning, Random forest

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