Understanding the Computer Science Student Experience Through the Lens of System Ecology

This research paper examines the experience of Computer Science students during their period of study in higher education institutions, based on the surrounding ecological system, using Bronfenbrenner's theory of human development as a guiding principle. The objective of the study was to uncover the significant elements of the student experience when the student environment is regarded and categorised as an ecology. We focused on eight distinct student development lifestyle categories that could have an influence on their experience: the student's own awareness of their experience as a concept, their pre-university phase, their transition to the university phase, their university peers and colleagues, their social background outside the university, their hosting department, their extracurricular activities, and their post-university phase, if any. The study involved the participation of 206 computer science students, yielding a suitable population for analysis. The survey contained 42 questions to address the aspects of the eight categories discussed above, with a range of questions for each category. The questions employed a mixed-methods methodology using open-ended and closed questions. For the open-ended questions, we followed a grounded theory coding analysis technique to identify common codes in the student's responses. Word fre-quencies and figures were also analyzed to gain insights into students' perspectives using sentiment analysis techniques. For the quantitative questions, response rates and summary statistics were calculated. The findings shed light on the pivotal factors that influence the student experience of Computer Science students, offering valuable insights to students, educators, administrators, and decision-makers. The study highlights the significance of environmental factors in shaping student experience in the CS discipline and proposes potential directions for future research in this field. The implications of these findings for the creation of more supportive and effective learning environments for Computer Science students are also discussed.