This work-in-progress research paper discusses issues of academic integrity which have long been a concern of education researchers and academic institutions within all fields of study. Academic integrity (AI) violations can consist of a broad range of student behaviors that are considered dishonest, including but not limited to plagiarism, copying others’ assignments, and paying for others to complete their work. A plethora of researchers have attempted to identify what underlying factors lead students to commit AI violations, and have identified several potential factors, including a lack of self-control, students’ ethical views of AI, perceived opportunities to commit AI violations, involvement in extracurricular activities, and students’ social groups.Another topic that has in recent times become a focal point of education research is students’ sense of belonging within their field of study. Researchers have identified several factors that contribute to students feeling less welcome within higher education, particularly within Engineering and Computer Science. Students who feel a lower sense of belonging have been identified as being at higher risk of performing poorly with their studies and retention rates for these students are historically lower. Despite this, little research has been conducted to examine where issues with students’ sense of belonging and their incidences of AI violations overlap. In this study, we attempt to try and better understand this relationship between students’ sense of belonging and AI violations by attempting to answer the following question: Can students’ sense of belonging within their discipline influence their propensity to violate academic integrity? We take up a student centered, restorative position, and choose to understand the cognitive underpinnings behind students’ choices to violate AI, with the goal of identifying if student outreach and more inclusive practices within Engineering and Computer Science can be utilized to prevent instances of AI violations. To accomplish this, we are employing a qualitative interview-based study of first-year students studying Computer Science at a large public university in the northeastern United States. We plan to analyze transcript data collected during interviews using Grounded Theory and Narrative Analysis methodologies. Our goal with this study is to draw awareness to additional underlying causes behind students deciding to violate AI, with the hope that this research will encourage academic institutions to employ a more preventative approach to handling AI issues by ensuring all students feel welcome and included within their chosen field of study, thereby helping prevent AI violations before they happen.
Mackay et al. (2022) studied this question.