Join us to discuss women’s elective choices in Computing on Monday 4th March at 2pm GMT

How can we increase participation of women in computing? How can we recruit and retain more women to study computing? Curricula are an obvious place to start. Understanding student motivations for their learning choices can help educators develop more effective programs of study. Join us to discuss a paper modeling women’s elective choices in computing by Steven Bradley, Miranda C. Parker, Rukiye Altin, Lecia Barker, Sara Hooshangi, Thom Kunkeler, Ruth G. Lennon, Fiona McNeill, Julià Minguillón, Jack Parkinson, Svetlana Peltsverger and Naaz Sibia from the Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE). From the abstract:

Evidence-based strategies suggest ways to reduce the gender gap in computing. For example, elective classes are valuable in enabling students to choose in which directions to expand their computing knowledge in areas aligned with their interests. The availability of electives of interest may also make computing programs of study more meaningful to women. However, research on which elective computing topics are more appealing to women is often class or institution specific. In this study, we investigate differences in enrollment within undergraduate-level elective classes in computing to study differences between women and men. The study combined data from nine institutions from both Western Europe and North America and included 272 different classes with 49,710 student enrollments. These classes were encoded using ACM curriculum guidelines and combined with the enrollment data to build a hierarchical statistical model of factors affecting student choice. Our model shows which elective topics are less popular with all students (including fundamentals of programming languages and parallel and distributed computing), and which elective topics are more popular with women students (including mathematical and statistical foundations, human computer interaction and society, ethics, and professionalism). Understanding which classes appeal to different students can help departments gain insight of student choices and develop programs accordingly. Additionally, these choices can also help departments explore whether some students are less likely to choose certain classes than others, indicating potential barriers to participation in computing.

We’ll be joined by some of the co-authors of the paper who will give us a five minute lightning talk summary to kick-off our discussion. As usual we’ll be meeting on zoom, all welcome, joining details at


  1. Steven Bradley, Miranda C. Parker, Rukiye Altin, Lecia Barker, Sara Hooshangi, Thom Kunkeler, Ruth G. Lennon, Fiona McNeill, Julià Minguillón, Jack Parkinson, Svetlana Peltsverger, Naaz Sibia (2023) ITiCSE-WGR ’23: Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education, Pages 196–226, DOI:10.1145/3623762.3633497

CC licensed image via

Join us to discuss spatial skills in engineering on Monday 9th May at 2pm BST

CC BY-SA licensed image of a Rubik’s cube via by Booyabazooka Wikimedia Commons

Spatial skills can be beneficial in engineering and computing, but how are they connected? Why are spatial abilities beneficial in engineering? Join us to discuss this via a paper on spatial skills training by Jack Parkinson and friends at the University of Glasgow. Here is the abstract:

We have been training spatial skills for Computing Science students over several years with positive results, both in terms of the students’ spatial skills and their CS outcomes. The delivery and structure of the training has been modified over time and carried out at several institutions, resulting in variations across each intervention. This article describes six distinct case studies of training deliveries, highlighting the main challenges faced and some important takeaways. Our goal is to provide useful guidance based on our varied experience for any practitioner considering the adoption of spatial skills training for their students.

see [1]

All welcome. As usual we’ll be meeting on zoom, details are in the slack channel Thanks to Steven Bradley for suggesting the paper


  1. Jack Parkinson, Ryan Bockmon, Quintin Cutts, Michael Liut, Andrew Petersen and Sheryl Sorby (2021) Practice report: six studies of spatial skills training in introductory computer science, ACM Inroads Volume 12, issue 4, pp 18–29 DOI: 10.1145/3494574

Join us to discuss failure rates in introductory programming courses on Monday 1st February at 2pm GMT

Icons made by freepik from

Following on from our discussion of ungrading, this month we’ll be discussing pass/fail rates in introductory programming courses. [1] Here is the abstract:

Vast numbers of publications in computing education begin with the premise that programming is hard to learn and hard to teach. Many papers note that failure rates in computing courses, and particularly in introductory programming courses, are higher than their institutions would like. Two distinct research projects in 2007 and 2014 concluded that average success rates in introductory programming courses world-wide were in the region of 67%, and a recent replication of the first project found an average pass rate of about 72%. The authors of those studies concluded that there was little evidence that failure rates in introductory programming were concerningly high.

However, there is no absolute scale by which pass or failure rates are measured, so whether a failure rate is concerningly high will depend on what that rate is compared against. As computing is typically considered to be a STEM subject, this paper considers how pass rates for introductory programming courses compare with those for other introductory STEM courses. A comparison of this sort could prove useful in demonstrating whether the pass rates are comparatively low, and if so, how widespread such findings are.

This paper is the report of an ITiCSE working group that gathered information on pass rates from several institutions to determine whether prior results can be confirmed, and conducted a detailed comparison of pass rates in introductory programming courses with pass rates in introductory courses in other STEM disciplines.

The group found that pass rates in introductory programming courses appear to average about 75%; that there is some evidence that they sit at the low end of the range of pass rates in introductory STEM courses; and that pass rates both in introductory programming and in other introductory STEM courses appear to have remained fairly stable over the past five years. All of these findings must be regarded with some caution, for reasons that are explained in the paper. Despite the lack of evidence that pass rates are substantially lower than in other STEM courses, there is still scope to improve the pass rates of introductory programming courses, and future research should continue to investigate ways of improving student learning in introductory programming courses.

Anyone is welcome to join us. As usual, we’ll be meeting on zoom, see for details.

Thanks to Brett Becker and Joseph Allen for this months #paper-suggestions via our slack channel at


  1. Simon, Andrew Luxton-Reilly, Vangel V. Ajanovski, Eric Fouh, Christabel Gonsalvez, Juho Leinonen, Jack Parkinson, Matthew Poole, Neena Thota (2019) Pass Rates in Introductory Programming and in other STEM Disciplines in ITiCSE-WGR ’19: Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education, Pages 53–71 DOI: 10.1145/3344429.3372502