Toward Understanding Students' Learning Performance in an Object-Oriented Programming Course: The Perspective of Program Quality

Qing Sun, Ji Wu*, Kaiqi Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

This pilot study examines how students' performance has evolved in an Object-oriented (OO) programming course and contributes to the learning analytic framework for similar programming courses in university curriculum. First, we briefly introduce the research background, a novel OO teaching practice with consecutive and iterative assignments consisting of programming and testing assignments. We propose a planned quantitative method for assessing students' gains in terms of programming performance and testing performance. Based on real data collected from students who engaged in our course, we use trend analysis to observe how students' performance has improved over the whole semester. By using correlation analysis, we obtain some interesting findings on how students' programming performance correlates with testing performance, which provides persuasive empirical evidence in integrating software testing practices into an Object-oriented programming curriculum. Then, we conduct an empirical study on how students' design competencies are represented by their program code quality changes over consecutive assignments by analyzing their submitted source code in the course system and the GitLab repository. Three different kinds of profiles are found in the students' program quality in the OO design level. The group analysis results reveal several significant differences in their programming performance and testing performance. Moreover, we conduct systematical explanations on how students' programming skill improvement can be attributed to their object-oriented design competency. By performing principal component analysis on software statistical data, a predictive OO metrics suite for both students' programming performance and their testing performance is proposed. The results show that these quality factors can serve as useful predictors of students' learning performance and can provide effective feedback to the instructors in the teaching practices.

Original languageEnglish
Article number8995543
Pages (from-to)37505-37517
Number of pages13
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Object-oriented Metrics
  • Object-oriented programming
  • empirical assessment
  • software testing

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