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, Sup...
Main Authors: | Carlos Giovanny Hidalgo Suarez, Jose Llanos, Víctor A. Bucheli |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2023-12-01
|
Series: | PeerJ Computer Science |
Subjects: | |
Online Access: | https://peerj.com/articles/cs-1689.pdf |
Similar Items
-
Prediction of Academic Performance at Undergraduate Graduation: Course Grades or Grade Point Average?
by: Ahmet Emin Tatar, et al.
Published: (2020-07-01) -
Systemic advantage has a meaningful relationship with grade outcomes in students’ early STEM courses at six research universities
by: Sarah D. Castle, et al.
Published: (2024-02-01) -
Gender gaps in grades versus grade penalties: why grade anomalies may be more detrimental for women aspiring for careers in biological sciences
by: Alysa Malespina, et al.
Published: (2023-02-01) -
Influence of a revision course and the gender of examiners on the grades of the final ENT exam – a retrospective review of 3961 exams
by: Grasl, Matthäus C., et al.
Published: (2015-10-01) -
Implementation of Alternative Grading Methods in a Mathematical Statistics Course
by: Brenna Curley, et al.
Published: (2023-08-01)