Abstract

The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.

Highlights

  • Introductory programming courses form the basis for the students in computer science major

  • Even though the decision tree appeared more useful than other classifiers, the results show a weak correlation between the social activities of the student and their final grades

  • This research aims to produce a prediction model that could correctly predict the performance of introductory programming students at an early stage of the semester

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Summary

Introduction

Introductory programming courses form the basis for the students in computer science major. The main objective of these courses is to empower the students with the fundamental skills required to develop computer programs that can solve real-world problems. Novice programming students face diverse types of complexities which result in high dropout and failure rates [1, 2]. In this context, the instructors may not be able to achieve the prime objectives of the course with the implementation of traditional teaching methodologies and may face various challenges related to the course’s nature, the students’ characteristics, and the adopted teaching methods [3]

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