How good is generative AI at passing exams? What does this tell us about how we could design better assessments? Join us on Monday 4th December at 2pm GMT (UTC) to discuss a paper on this by Joyce Mahon, Brian Mac Namee and Brett Becker at University College Dublin published at UKICER earlier this year.  From the abstract:
We investigate the capabilities of ChatGPT (GPT-4) on second level (high-school) computer science examinations: the UK A-Level and Irish Leaving Certificate. Both are national, government-set / approved, and centrally assessed examinations. We also evaluate performance differences in exams made publicly available before and after the ChatGPT knowledge cutoff date, and investigate what types of question ChatGPT struggles with.
We find that ChatGPT is capable of achieving very high marks on both exams and that the performance difference before and after the knowledge cutoff date are minimal. We also observe that ChatGPT struggles with questions involving symbols or images, which can be mitigated when in-text information ‘fills in the gaps’. Additionally, GPT-4 performance can be negatively impacted when an initial inaccurate answer leads to further inaccuracies in subsequent parts of the same question. Finally, the element of choice on the Leaving Certificate is a significant advantage in achieving a high grade. Notably, there are minimal occurrences of hallucinations in answers and few errors in solutions not involving images.
These results reveal several strengths and weaknesses of these exams in terms of how generative AI performs on them and have implications for exam design, the construction of marking schemes, and could also shift the focus of what is examined and how.
We’ll be joined by the papers lead author Joyce, who will give us a lightning talk summary of her paper to start our discussion. All welcome, as usual we’ll be meeting on zoom details at sigcse.cs.manchester.ac.uk/join-us
Joyce Mahon, Brian MacNamee and Brett A. Becker (2023) No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations. In The United Kingdom and Ireland Computing Education Research conference (UKICER 2023), September 07–08, 2023, Swansea, Wales UK. ACM, New York, NY, USA, 7 pages. DOI: 10.1145/3610969.3610982
Programming is hard, or at least it used to be. AI code generators like Amazon’s CodeWhisperer, DeepMind’s AlphaCode, GitHub’s CoPilot, Replit’s Ghostwriter and many others now make programming easier, at least for some people, some of the time. What opportunities and challenges do these new tools present for educators? Join us on Zoom to discuss an award winning paper by Brett Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather and Eddie Antonio Santos at University College Dublin, the University of Auckland and Abilene Christian University on this very topic.  We’ll be joined by two of the co-authors who will present a lightning talk to kick-off our discussion, for our monthly ACM journal club meetup. Here’s the abstract of his paper:
The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on overcoming otherwise mitigating the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.
Brett A. Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, Eddie Antonio Santos (2023) Programming Is Hard – Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation in Proceedings of the 54th ACM Technical Symposium on Computer Science Education: SIGCSE 2023, pages 500–506, DOI: 10.1145/3545945.3569759
Automatic code generators have been with us a while, but how do modern AI powered bots perform on introductory programming assignments? Join us to discuss the implications of the OpenAI Codex on introductory programming courses on Monday 4th July at 2pm BST. We’ll be discussing a paper by James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly and James Prather  for our monthly SIGCSE journal club meetup on zoom. Here is the abstract:
Recent advances in artificial intelligence have been driven by an exponential growth in digitised data. Natural language processing, in particular, has been transformed by machine learning models such as OpenAI’s GPT-3 which generates human-like text so realistic that its developers have warned of the dangers of its misuse. In recent months OpenAI released Codex, a new deep learning model trained on Python code from more than 50 million GitHub repositories. Provided with a natural language description of a programming problem as input, Codex generates solution code as output. It can also explain (in English) input code, translate code between programming languages, and more. In this work, we explore how Codex performs on typical introductory programming problems. We report its performance on real questions taken from introductory programming exams and compare it to results from students who took these same exams under normal conditions, demonstrating that Codex outscores most students. We then explore how Codex handles subtle variations in problem wording using several published variants of the well-known “Rainfall Problem” along with one unpublished variant we have used in our teaching. We find the model passes many test cases for all variants. We also explore how much variation there is in the Codex generated solutions, observing that an identical input prompt frequently leads to very different solutions in terms of algorithmic approach and code length. Finally, we discuss the implications that such technology will have for computing education as it continues to evolve, including both challenges and opportunities. (see accompanying slides and sigarch.org/coping-with-copilot/)
James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, James Prather (2022) The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming ACE ’22: Australasian Computing Education Conference Pages 10–19 DOI:10.1145/3511861.3511863
Students sense of belonging has been shown to be associated with many attributes such as motivation and persistence. But what makes people feel like they belong in Computer Science? Join us on Monday 6th December at 2pm GMT to discuss belonging via a paper by Catherine Mooney and Brett Becker.  This won a best paper award at SIGCSE 2021.
[There will be no SIGCSE journal club in November, however, we’ll be back in December as usual.]
Sense of belonging, or belongingness, describes how accepted one feels in their academic community and is an important factor in creating inclusive learning environments. Belongingness is influenced by many factors including: students’ backgrounds and experiences; other people; environments (physical and virtual); academic discipline; external factors such as local, regional, and global issues; and time. 2020 has been dominated by several major events including the COVID-19 pandemic which dramatically impacted education. The Black Lives Matter movement has further raised global awareness of equality, diversity and inclusion not just in society, but in educational contexts. Climate change concerns, and politically charged news are also increasingly affecting our students.
We have been monitoring our undergraduate computing students’ sense of belonging for over three years, providing us with a unique opportunity to gauge recent changes during the pandemic. Our results surprised us. We found statistically significant reductions in the belongingness of students identifying as men as well as those not identifying as being part of a minority. However, investigating intersectionality of self-identified gender and minority status revealed more complicated and nuanced trends, illustrating important shifts in the belongingness of our students that we are only beginning to understand.
Catherine Mooney and Brett Becker (2021) Investigating the Impact of the COVID-19 Pandemic on Computing Students’ Sense of Belonging. SIGCSE ’21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, March 2021 Pages 612–618 DOI:10.1145/3408877.3432407
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.