Why do some students achieve more than others? Students goals, their belief in their ability to reach those goals and their prior experience are key factors. But how do they interplay? Join us for our monthly ACM SIGCSE journal club meetup on Zoom to discuss a prize-winning paper  on this topic by Hannu Pesonen, Juho Leinonen, Lassi Haaranen and Arto Hellas from Aalto University in Finland and the University of Auckland. From the abstract:
We explore achievement goal orientations, self-efficacy, gender, and prior experience, and look into their interplay in order to understand their contributions to course performance. Our results provide evidence for the appropriateness of the three-factor achievement goal orientation model (performance, mastery approach, mastery avoidance) over the more pervasive four-factor model. We observe that the aspects and the model factors correlate with course achievement. However, when looking into the interplay of the aspects and the model factors, the observations change and the role of, for example, self-efficacy as an aspect contributing to course achievement diminishes. Our study highlights the need to further explore the interplay of aspects contributing to course achievement.
We’ll be joined by one of the papers co-authors, Hannu, who’ll give a lightning talk summary to kick off our discussion. This paper won a best paper award at ukicer.com this year. All welcome, meeting details at sigcse.cs.manchester.ac.uk/join-us
Hannu Pesonen, Juho Leinonen, Lassi Haaranen, and Arto Hellas (2023) Exploring the Interplay of Achievement Goals, Self-Efficacy, Prior Experience and Course Achievement. In The United Kingdom and Ireland Computing Education Research (UKICER) conference (UKICER 2023), September 07–08, 2023, Swansea, Wales UK. ACM, New York, NY, USA, 7 pages. DOI: 10.1145/3610969.3611178
Following on from our discussion of ungrading, this month we’ll be discussing pass/fail rates in introductory programming courses.  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.