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

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...

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Bibliographic Details
Published in:PeerJ. Computer science 2023-12, Vol.9, p.e1689-e1689, Article e1689
Main Authors: Hidalgo Suarez, Carlos Giovanny, Llanos, Jose, Bucheli, Víctor A
Format: Article
Language:English
Subjects:
Academic achievement
Accuracy
Algorithms
Analysis
Artificial Intelligence
Classification
Computer Education
Computer programming
Course grade
CS1
Data mining
Datasets
Decision making
Forecasts and trends
Grades (Scholastic marks)
Learning analytics
Literature reviews
Machine learning
Neural networks
Predicting final grade
Programming Languages
Regression analysis
Regression model
Regression models
Students
Support vector machines
Testing
Citations: Items that this one cites
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Summary: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 ( ) 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.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.1689