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

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. INDEX TERMS Object-oriented programming, Object-oriented Metrics, software testing, empirical assessment.

Identifying Difficulties of Software Modeling Through Class Diagrams: A Long-Term Comparative Analysis

“…Based on the review of the literature and the analysis of the work presented by the students, the author expresses that the students have difficulties to close the gap between the problem descriptions and the code, however, the author is not explicit in defining which are the problem student face when transferring diagrams to code. In [21], through a planned quantitative method and correlation analysis, the authors conclude that students improved their programming performance thanks to their previous designs. In addition, in the same study, the researches based on test results and object-oriented metrics parameters conduct an empirical study using the hierarchical clustering technique to compare design quality of students and their performance in terms of correctness.…”

“…There are a couple of studies [20], [21], where the authors state that there is a relationship between the performance of students obtained in tests related to software design questions and produce a more reliable program code. In [20], the author studies the relationship between the ability to create a design with the ability to program from the analysis of UML diagrams versus source code.…”

Real time multi feature performance analysis model for efficient prediction of student performance using neural networks

“…Predicting Student Performance in an Educational Game Using a Hidden Markov Model [7], shows that students' knowledge throughout the intervention can be estimated by time-series analysis using a hidden Markov model (HMM). Toward Understanding Students' Learning Performance in an Object-Oriented Programming Course: The Perspective of Program Quality [8], 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.…”

Explaining Individual and Collective Programming Students’ Behavior by Interpreting a Black-Box Predictive Model

“…As an attempt to alleviate that, multiple recent studies [11], [12], [16], [23], [46], [46], [47], [52], [53], [59] proposed methods to predict CS1 students' performance early on. Knowing about student performance in advance can be useful for many reasons, for example, instructors can apply specific actions to help learners who are struggling, as well as provide more challenging activities to high-achievers [11], [58], [67].…”

“…Previously, most methods to predict CS1 students' performance were based on a static analysis of the students' data, such as their high school grades, age, gender [1]. However, students' behaviour is dynamic and, hence, can change over time, supporting the need for data-driven analysis [11], [49], [53], [67]. Along these lines, the use of Machine Learning (ML) over data collected from e-learning systems leveraged approaches and methods to tackle the performance prediction problem [11].…”