Comparison of Student Engagement and Exam Success at Introductory Computer Programming Classes on Faculty of Electrical Engineering University of Ljubljana in Years 2023 and 2024 after Mandatory Practical Exam Introduction

<p dir="ltr">This study investigates the impact of introducing a mandatory practical programming exam on student learning outcomes in introductory programming courses. To facilitate structured coding practice and scalable automated feedback, we developed Programmers' Interactive...

Full description

Saved in:
Bibliographic Details
Main Author: Žiga Rojec (22175137) (author)
Other Authors: Iztok Fajfar (22788637) (author), Janez Puhan (22788638) (author)
Published: 2025
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:<p dir="ltr">This study investigates the impact of introducing a mandatory practical programming exam on student learning outcomes in introductory programming courses. To facilitate structured coding practice and scalable automated feedback, we developed Programmers' Interactive Virtual Onboarding (PIVO), a novel Automated Programming Assessment System (APAS). Traditional programming curricula often prioritize theoretical concepts, limiting practical coding opportunities and immediate feedback, resulting in poor skill retention and proficiency. By integrating mandatory practical assessments together with voluntary, self-driven programming tasks through PIVO, we aimed to enhance student engagement, programming proficiency, and overall academic performance.</p><p><br></p><p dir="ltr">Results demonstrate that mandatory practical exams significantly reduced failure rates, and statistical analyses revealed a strong correlation between students' voluntary engagement in non-mandatory coding exercises and their performance in both theoretical and practical examinations. These findings suggest that structured, automated practice assessments effectively improve algorithmic thinking and problem-solving capabilities. Future research directions include exploring test-driven learning approaches and adapting assessment strategies to address emerging challenges posed by AI-generated code submissions.</p>