Blog post based on the following paper: Barr, M., & Kallia, M. (2022). Why Students Drop Computing Science: Using Models of Motivation to Understand Student Attrition and Retention. Koli Calling ’22: 22nd Koli Calling International Conference on Computing Education Research, 1–6. https://doi.org/10.1145/3564721.3564733
We know that, in many parts of the world, there is a shortage of Computing Science graduates. And, we are particularly short on female Computing Science (CS) graduates. Given these disparities in participation in CS, and part of the puzzle is understanding why students drop out of the subject.
The reasons for students dropping CS are multi-dimensional (see Kinnunen and Malmi, 2006; Petersen et al., 2016), but gender is often a factor. For starters, there’s the “macho geeks problem” (Dee et al., 2009), referring to the geeky “know it all” male culture that dominates CS classrooms. Meanwhile, gender stereotypes and women’s experience of male-dominated work and study environments make this a self-perpetuating problem (Taylor-Smith et al., 2022). And, there’s a lack of support for women in CS and tech (DuBow et al., 2016).
In our recent study, we draw from two theoretical models to try to understand why students – and women, in particular – are leaving CS behind. The first is Eccles’ expectancy-value model of achievement which may be used explain students’ choices, persistence, and performance in a subject. The second is the Generalized Internal/External Frame of Reference Model, developed by Marsh, which concerns students’ domain-specific self-concepts of ability. These models are explained in more detail in our paper, and in the video below.
We surveyed students at our university who had dropped Computing Science, asking why they had done so. Our analysis of the responses revealed that both the expectancy-value model of achievement and the internal/external frame of reference model could shed some light on students’ decisions to drop CS.
The Eccles expectancy-value model suggests that achievement-related choices (like educational choices) are dependent on our expectations of success, and the subjective value we place on a task.
Indeed, our findings do fit this model but, surprisingly, they further suggest that expectancy of success – which has often been studied in relation to CS – may not be such a strong a predictor; only 13% of our participants cited this factor as the reason for not continuing in CS.
In fact, for most participants, the task value was the most important factor, and particularly, the utility value of studying CS. This strongly suggests that highlighting the value of CS to students’ future goals is critical for attainment.
An important finding of our research is that students frequently cited the perceived cost of participating in CS as a reason for dropping the subject. This cost was expressed in terms of both time (e.g., time away from other subjects or activities) and affect (e.g. feelings of stress and frustration). Perceived cost is often overlooked in empirical studies, and our findings suggest cost as an important factor that calls for further investigation.
Marsh’s internal/external frame of reference model complements the observations above, especially those related to cost. According to this model, students make comparisons between their own performance in one subject versus another, and between their academic performance and that of their peers.
Interestingly, comparisons between a student’s perceived ability and that of
their peers, were only reported by female students; male students did not refer to comparisons with their peers’ abilities as a factor in dropping CS.
Finally, other factors like perceived course difficulty and social concerns were cited as reasons for dropping CS, and significantly more so by female students (who referred to feelings of not belonging and how difficult it was to relate to male students who seem more knowledgeable).
Our results suggest it would be useful to investigate further the interplay between all of these factors and CS students’ motivation and academic choices. Our future work aims to delineate these relationships by examining them with respect to gender and different minority groups – there were clear differences between genders here, and we know that gender balance in CS is a significant, ongoing issue. There is more work to be done!
Barr, M., & Kallia, M. (2022). Why Students Drop Computing Science: Using Models of Motivation to Understand Student Attrition and Retention. Koli Calling ’22: 22nd Koli Calling International Conference on Computing Education Research, 1–6. https://doi.org/10.1145/3564721.3564733
Dee, H. M., Petrie, K. E., Boyle, R. D., & Pau, R. (2009). Why are we still here? Experiences of successful women in computing. ACM SIGCSE Bulletin, 41(3), 233–237. https://doi.org/10.1145/1595496.1562951
DuBow, W., Weidler-Lewis, J., & Kaminsky, A. (2016). Multiple factors converge to influence women’s persistence in computing: A qualitative analysis of persisters and nonpersisters. 2016 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 1–7. https://doi.org/10.1109/RESPECT.2016.7836161
Kinnunen, P., & Malmi, L. (2006). Why students drop out CS1 course? Proceedings of the Second International Workshop on Computing Education Research, 97–108. https://doi.org/10.1145/1151588.1151604
Petersen, A., Craig, M., Campbell, J., & Tafliovich, A. (2016). Revisiting why students drop CS1. Proceedings of the 16th Koli Calling International Conference on Computing Education Research, 71–80. https://doi.org/10.1145/2999541.2999552
Taylor-Smith, E., Barnett, C., Smith, S., Barr, M., & Shankland, C. (2022). Participant-centred planning Framework for effective gender balance activities in tech. Proceedings of the 2022 Conference on United Kingdom & Ireland Computing Education Research, 1–7. https://doi.org/10.1145/3555009.3555016