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The study aims to predict student success in introductory computing based on high school coursework data.

May 16, 20260 citationsOpen Access

Predicting Success in Introductory Computing Course using Senior High School Specialized and Applied Coursework Data

Key Points

The study aims to predict student success in introductory computing based on high school coursework data.
Employed a quantitative predictive modeling approach using educational data mining techniques.
Developed a decision tree classifier using academic records from 258 first-year IT students.
Evaluated model performance with 10-fold cross-validation.
Achieved an accuracy of 84.5%, showing strong predictive capability.
Specialized coursework was identified as the most significant predictor of student success.
Applied coursework contributed meaningfully but was less significant compared to specialized coursework.

Abstract

Abstract Objectives: This study aims to predict student success in an Introduction to Computing course using Senior High School (SHS) specialized and applied coursework data, with particular focus on graduates from the Technical-Vocational-Livelihood Information and Communications Technology (TVL-ICT) track. Methods: A quantitative predictive modeling approach was employed using educational data mining techniques. A decision tree classifier was developed based on the academic records of 258 first-year Bachelor of Science in Information Technology students. The model utilized grades from three applied courses and two specialized courses as input features, with student performance (pass/fail) as the target variable. Model performance was evaluated using 10-fold cross-validation. Findings: The results revealed that the model achieved an accuracy of 84.5%, indicating strong predictive capability. Among the predictors, specialized coursework emerged as the most significant factor influencing student success, while applied coursework demonstrated a secondary yet meaningful contribution. Novelty: This study contributes to the limited body of research linking SHS TVL-ICT academic preparation directly to college-level computing success through predictive modeling. This research specifically isolates the predictive value of specialized and applied SHS coursework using a decision tree approach. The study introduces a data-driven framework for early identification of at-risk students Keywords: Predictive modelling, Senior high school, Computing, Coursework, Decision tree

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