What paper should we discuss on Monday 10th May at 2pm BST?

Our next journal club (see the upcoming events page) is scheduled for Monday 10th May at 2pm BST. What paper should we discuss? We could pick one of the nine best papers from SIGCSE 2021 last month (listed below). As usual, you can send us your paper suggestions via our slack channel, via twitter @ukicse, email me or post a comment below by Friday 16th April at 6pm BST.

Best Papers for Computing Education Research

  1. Real Talk: Saturated Sites of Violence in CS Education. Yolanda A. Rankin, Florida State University; Jakita O. Thomas, Auburn University; Sheena Erete, DePaul University
  2. Investigating the Impact of the COVID-19 Pandemic on Computing Students’ Sense of Belonging. Catherine Mooney, University College Dublin; Brett A. Becker, University College Dublin
  3. Superficial Code-guise: Investigating the Impact of Surface Feature Changes on Students’ Programming Question Scores. Max Fowler, University of Illinois at Urbana-Champaign; Craig Zilles, University of Illinois at Urbana-Champaign

Best Papers for Experience Reports and Tools

  1. How a Remote Video Game Coding Camp Improved Autistic College Students’ Self-Efficacy in Communication. Andrew Begel, Microsoft Research; James Dominic, Clemson University; Conner Phillis, KeyMark, Inc.; Thomas Beeson, Clemson University; Paige Rodeghero, Clemson University
  2. Inside the Mind of a CS Undergraduate TA: A Firsthand Account of Undergraduate Peer Tutoring in Computer Labs. Julia M. Markel, UC San Diego; Philip J. Guo, UC San Diego
  3. Understanding Immersive Research Experiences that Build Community, Equity, and Inclusion. Audrey Rorrer, UNC Charlotte; Breauna Spencer, University of California, Irvine; Sloan Davis, Google; Sepi Hejazi Moghadam, Google; Deborah Holmes, UNC Charlotte; Cori Grainger, Google

Best Papers for Positions and Curriculum Initiatives

  1. Creating a Multifarious Cyber Science Major. Raymond W. Blaine, U.S. Military Academy; Jean R. S. Blair, U.S. Military Academy; Christa M. Chewar, U.S. Military Academy; Rob Harrison, U.S. Military Academy; James J. Raftery, U.S. Military Academy; Edward Sobiesk, U.S. Military Academy
  2. Confronting Inequities in Computer Science Education: A Case for Critical Theory. Aleata Hubbard Cheuoua, WestEd
  3. Developing an Interdisciplinary Data Science Program. Mariam Salloum, University of California, Riverside; Daniel Jeske, University of California, Riverside; Wenxiu Ma, University of California, Riverside; Vagelis Papalexakis, University of California, Riverside; Christian Shelton, University of California, Riverside; Vassilis Tsotras, University of California, Riverside; Shuheng Zhou, University of California, Riverside

There’s also Nicola’s suggestion from last month on What Do We Think We Think We Are Doing? Metacognition and Self-Regulation in Programming by James Prather, Brett A. Becker, Michelle Craig, Paul Denny, Dastyni Loksa, and Lauren Margulieux from ICER 2020 doi.org/gh3qm8.

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

Icons made by freepik from flaticon.com

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