Tag Archives: Juho Leinonen

Join us on Monday 1st December at 2pm to discuss the Role of Generative AI in Software Student Collaboration

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What can students learn from Generative AI chatbots like ChatGPT? What roles can chatbots play in collaborative engineering? Tutor? Facilitator? Pair programmer? Task master? Vibe coder? Coursework cheater? Or something else? Join us at 2pm GMT on Monday 1st December to discuss a position paper on this published at ITiCSE earlier this year. [1] From the abstract:

Collaboration is a crucial part of computing education. The increase in AI capabilities over the last couple of years is bound to profoundly affect all aspects of systems and software engineering, including collaboration. In this position paper, we consider a scenario where AI agents would be able to take on any role in collaborative processes in computing education. We outline these roles, the activities and group dynamics that software development currently include, and discuss if and in what way AI could facilitate these roles and activities. The goal of our work is to envision and critically examine potential futures. We present scenarios suggesting how AI can be integrated into existing collaborations. These are contrasted by design fictions that help demonstrate the new possibilities and challenges for computing education in the AI era.

We’ll be joined by one of the papers authors, Juho Leinonen, who’ll give us a lightning talk summary of the research. All welcome, meeting URL is public at zoom.us/j/96465296256 (meeting ID 9646-5296-256) but the password is private and pinned in the slack channel which you can join by following the instructions at sigcse.cs.manchester.ac.uk/join-us

(Cite this article using DOI:10.59350/c26zw-0tm88 provided by rogue-scholar.org)

References

  1. Natalie Kiesler, Jacqueline Smith, Juho Leinonen, Armando Fox, Stephen MacNeil, Petri Ihantola (2025) The Role of Generative AI in Software Student CollaborAItion, ITiCSE 2025: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education, Pages 72 – 78, DOI:10.1145/3724363.3729040

Join us to discuss the goals and self-efficacy of students on Monday 2nd October at 2pm BST (UTC+1)

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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 [1] 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

References

  1. 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

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

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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 sigcse.cs.manchester.ac.uk/join-us for details.

Thanks to Brett Becker and Joseph Allen for this months #paper-suggestions via our slack channel at uk-acm-sigsce.slack.com.

References

  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