Factors Influencing Performance of Students in Software Automated Test Tools Course

TL;DR

This study investigates factors affecting student performance in an automated testing course, using machine learning models to predict outcomes and identify key influencing factors, with logistic regression achieving 90% accuracy.

Contribution

It introduces a predictive model for student performance in automated testing courses and identifies key factors influencing success, such as assessment type and prerequisite courses.

Findings

Logistic regression achieved 90% accuracy in predicting student performance.

Assessment type and prerequisite courses significantly impact student success.

Machine learning can effectively identify key performance factors in software testing education.

Abstract

Formal software testing education is important for building efficient QA professionals. Various aspects of quality assurance approaches are usually covered in courses for training software testing students. Automated Test Tools is one of the core courses in the software testing post-graduate curriculum due to the high demand for automated testers in the workforce. It is important to understand which factors are affecting student performance in the automated testing course to be able to assist the students early on based on their needs. Various metrics that are considered for predicting student performance in this testing course are student engagement, grades on individual deliverables, and prerequisite courses. This study identifies the impact of assessing students based on individual vs. group activities, theoretical vs. practical components, and the effect of having taken prerequisite courses in their final grade. To carry out this research, student data was collected from the automated test tools course of a community college-based postgraduate certificate program in software testing. The dataset contained student records from the years 2021 to 2022 and consisted of information from five different semesters. Various machine learning algorithms were applied to develop an effective model for predicting students performance in the automated software testing tools course, and finally, important features affecting the students performance were identified. The predictive performance model of the automated test tools course that was developed by applying the logistic regression technique, showed the best performance, with an accuracy score of 90%.