Join us to re-examine inequalities in Computer Science participation on Monday 4th October at 2pm BST

Loaded scales image by Carole J. Lee on Wikimedia Commons w.wiki/42Rp

It’s no secret that both Computer Science and engineering have inequalities in their participation. Join us to re-examine and discuss these inequalities via a paper by Maria Kallia and Quintin Cutts [1] on Monday 4th October at 2pm BST. This won a best paper award at ICER 2021. From the abstract:

Concerns about participation in computer science at all levels of education continue to rise, despite the substantial efforts of research, policy, and world-wide education initiatives. In this paper, which is guided by a systematic literature review, we investigate the issue of inequalities in participation by bringing a theoretical lens from the sociology of education, and particularly, Bourdieu’s theory of social reproduction. By paying particular attention to Bourdieu’s theorising of capital, habitus, and field, we first establish an alignment between Bourdieu’s theory and what is known about inequalities in computer science (CS) participation; we demonstrate how the factors affecting participation constitute capital forms that individuals possess to leverage within the computer science field, while students’ views and dispositions towards computer science and scientists are rooted in their habitus which influences their successful assimilation in computer science fields. Subsequently, by projecting the issue of inequalities in CS participation to Bourdieu’s sociological theorisations, we explain that because most interventions do not consider the issue holistically and not in formal education settings, the reported benefits do not continue in the long-term which reproduces the problem. Most interventions have indeed contributed significantly to the issue, but they have either focused on developing some aspects of computer science capital or on designing activities that, although inclusive in terms of their content and context, attempt to re-construct students’ habitus to “fit” in the already “pathologized” computer science fields. Therefore, we argue that to contribute significantly to the equity and participation issue in computer science, research and interventions should focus on restructuring the computer science field and the rules of participation, as well as on building holistically students’ computer science capital and habitus within computer science fields.

A presentation video by Maria of the paper from ICER 2021


All welcome. As usual, we’ll be meeting on zoom. Thanks to Steven Bradley for suggesting this months paper.

References

  1. Maria Kallia and Quintin Cutts (2021) Re-Examining Inequalities in Computer Science Participation from a Bourdieusian Sociological Perspective. In Proceedings of the 17th ACM Conference on International Computing Education Research (ICER) 2021 Pages 379–392, 10.1145/3446871.3469763

Join us to discuss the tyranny of content on Monday 5th July at 2pm BST

CC-BY-SA image of Bill Gates by Kuhlmann MSC via Wikimedia Commons w.wiki/3W7k

If content is king, then his rule is tyrannical. Bill Gates once remarked that “Content is King” but In the kingdom of education, how much do educators oppressively inflict content on their learners? What can be done to reduce the tyranny of content? We’ll be discussing this via a paper by Christina I. Petersen et al, here’s the abstract:

Instructors have inherited a model for conscientious instruction that suggests they must cover all the material outlined in their syllabus, and yet this model frequently diverts time away from allowing students to engage meaningfully with the content during class. We outline the historical forces that may have conditioned this teacher-centered model as well as the disciplinary pressures that inadvertently reward it. As a way to guide course revision and move to a learner-centered teaching approach, we propose three evidence-based strategies that instructors can adopt: 1) identify the core concepts and competencies for your course; 2) create an organizing framework for the core concepts and competencies; and 3) teach students how to learn in your discipline. We further outline examples of actions that instructors can incorporate to implement each of these strategies. We propose that moving from a content-coverage approach to these learner-centered strategies will help students better learn and retain information and apply it to new situations.

All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details.

References

  1. Petersen, Christina I.; Baepler, Paul; Beitz, Al; Ching, Paul; Gorman, Kristen S.; Neudauer, Cheryl L.; Rozaitis, William; Walker, J. D.; Wingert, Deb; Reiness, C. Gary (2020). The Tyranny of Content: “Content Coverage” as a Barrier to Evidence-Based Teaching Approaches and Ways to Overcome It. CBE—Life Sciences Education19 (2): ar17. doi:10.1187/cbe.19-04-0079

Join us to discuss cognitive load on Monday 7th June at 2pm

Cognitive Load Theory provides a basis for understanding the learning process. It has been widely used to improve the teaching and learning of many subjects including Computer Science. But how can it help us build better collaborative learning experiences? Join us to discuss via a paper by Paul Kirschner, John Sweller, Femke Kirschner & Jimmy Zambrano R. [1] From the abstract:

Cognitive load theory has traditionally been associated with individual learning. Based on evolutionary educational psychology and our knowledge of human cognition, particularly the relations between working memory and long-term memory, the theory has been used to generate a variety of instructional effects. Though these instructional effects also influence the efficiency and effectiveness of collaborative learning, be it computer supported or face-to-face, they are often not considered either when designing collaborative learning situations/environments or researching collaborative learning. One reason for this omission is that cognitive load theory has only sporadically concerned itself with certain particulars of collaborative learning such as the concept of a collective working memory when collaborating along with issues associated with transactive activities and their concomitant costs which are inherent to collaboration. We illustrate how and why cognitive load theory, by adding these concepts, can throw light on collaborative learning and generate principles specific to the design and study of collaborative learning.

Thanks to Nicola Looker for suggesting this months paper. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details.

References

  1. Kirschner, Paul A.; Sweller, John; Kirschner, Femke; Zambrano R., Jimmy (2018). “From Cognitive Load Theory to Collaborative Cognitive Load Theory”. International Journal of Computer-Supported Collaborative Learning13 (2): 213–233. DOI:10.1007/s11412-018-9277-y

Join us to discuss what goes on in the mind of Teaching Assistants on Monday 10th May at 2pm BST

Thinking icon via flaticon.com

Both graduate and undergraduate teaching assistants (TAs) are crucial to facilitating students learning. What goes on inside the mind of a teaching assistant? How can understanding this help us train TA’s better for the roles they play in education? Join us to discuss via a paper by Julia M. Markel and Philip Guo. [1] From the abstract:

As CS enrolments continue to grow, introductory courses are employing more undergraduate TAs. One of their main roles is performing one-on-one tutoring in the computer lab to help students understand and debug their programming assignments. What goes on in the mind of an undergraduate TA when they are helping students with programming? In this experience report, we present firsthand accounts from an undergraduate TA documenting her 36 hours of in-lab tutoring for a CS2 course, where she engaged in 69 one-on-one help sessions. This report provides a unique perspective from an undergraduate’s point-of-view rather than a faculty member’s. We summarise her experiences by constructing a four-part model of tutoring interactions: a) The tutor begins the session with an initial state of mind (e.g., their energy/focus level, perceived time pressure). b) They observe the student’s outward state upon arrival (e.g., how much they seem to care about learning). c) Using that observation, the tutor infers what might be going on inside the student’s mind. d) The combination of what goes on inside the tutor’s and student’s minds affects tutoring interactions, which progress from diagnosis to planning to an explain-code-react loop to post-resolution activities. We conclude by discussing ways that this model can be used to design scaffolding for training novice TAs and software tools to help TAs scale their efforts to larger classes.

This paper was one of nine best papers at SIGCSE 2021, there’s a video of the paper presentation on pathable.sigcse2021.org. All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details.

References

  1. Markel, Julia M. and Guo, Philip (2021) Inside the Mind of a CS Undergraduate TA: A Firsthand Account of Undergraduate Peer Tutoring in Computer Labs SIGCSE ’21: Proceedings of the 52nd ACM Technical Symposium on Computer Science EducationMarch 2021 Pages 502–508 DOI: 10.1145/3408877.3432533 (open access)

Join us to discuss learning sciences for computing education on Monday 12th April at 2pm BST

Scientist icon made by Eucalyp flaticon.com

Learning sciences aims to improve our theoretical understanding of how people learn while computing education investigates with how people learn to compute. Historically, these fields existed independently, although attempts have been made to merge them. Where do these disciplines overlap and how can they be integrated further? Join us to discuss learning sciences for computing education via a paper by Lauren Margulieux, Brian Dorn and Kristin Searle, from the abstract:

This chapter discusses potential and current overlaps between the learning sciences and computing education research in their origins, theory, and methodology. After an introduction to learning sciences, the chapter describes how both learning sciences and computing education research developed as distinct fields from cognitive science. Despite common roots and common goals, the authors argue that the two fields are less integrated than they should be and recommend theories and methodologies from the learning sciences that could be used more widely in computing education research. The chapter selects for discussion one general learning theory from each of cognition (constructivism), instructional design (cognitive apprenticeship), social and environmental features of learning environments (sociocultural theory), and motivation (expectancy-value theory). Then the chapter describes methodology for design-based research to apply and test learning theories in authentic learning environments. The chapter emphasizes the alignment between design-based research and current research practices in computing education. Finally, the chapter discusses the four stages of learning sciences projects. Examples from computing education research are given for each stage to illustrate the shared goals and methods of the two fields and to argue for more integration between them.

There’s a 5 minute summary of the chapter ten minutes into the video below:



All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details. Thanks to this months paper suggestions from Sue Sentance and Nicola Looker.

References

  1. Margulieux, Lauren E.; Dorn, Brian; Searle, Kristin A. (2019). “Learning Sciences for Computing Education“: 208–230. doi:10.1017/9781108654555.009. in In S. A. Fincher & A. V. Robins (Eds.) The Cambridge Handbook of Computing Education Research. Cambridge, UK: Cambridge University Press

Join us to discuss teaching social responsibility and justice in Computer Science on Monday 1st March at 2pm GMT

Scales of justice icon made by monkik from flaticon.com

With great power comes great responsibility. [1] Given their growing power in the twenty-first century, computer scientists have a duty to society to use that power responsibly and justly. How can we teach this kind of social responsibility and ethics to engineering students? Join us to discuss teaching social justice in computer science via a paper by Rodrigo Ferreira and Moshe Vardi at Rice University in Houston, Texas published in the sigcse2021.sigcse.org conference [2]. From the abstract of the preprint:

As ethical questions around the development of contemporary computer technologies have become an increasing point of public and political concern, computer science departments in universities around the world have placed renewed emphasis on tech ethics undergraduate classes as a means to educate students on the large scale social implications of their actions. Committed to the idea that tech ethics is an essential part of the undergraduate computer science educational curriculum, at Rice University this year we piloted a redesigned version of our Ethics and Accountability in Computer Science class. This effort represents our first attempt at implementing a “deep” tech ethics approach to the course.

Incorporating elements from philosophy of technology, critical media theory, and science and technology studies, we encouraged students to learn not only ethics in a “shallow” sense, examining abstract principles or values to determine right and wrong, but rather looking at a series of “deeper” questions more closely related to present issues of social justice and relying on a structural understanding of these problems to develop potential socio-technical solutions. In this article, we report on our implementation of this redesigned approach. We describe in detail the rationale and strategy for implementing this approach, present key elements of the redesigned syllabus, and discuss final student reflections and course evaluations. To conclude, we examine course achievements, limitations, and lessons learned toward the future, particularly in regard to the number escalating social protests and issues involving Covid-19.

This paper got me thinking:

Houston, we’ve had your problem!

After paging the authors in Houston with the message above there was radio silence.

Beep - beep - beep [white noise] Beep - beep - beep...

Hello Manchester, this is Houston, Can we join you?

So we’re delighted to be joined LIVE by the authors of the paper Rodrigo Ferreira and Moshe Vardi from Houston, Texas. They’ll give a lightning talk outlining the paper before we discuss it together in smaller break out groups.

Their paper describes a problem everyone in the world has had in teaching ethics in Computer Science recently. How can we make computing more ethical?

All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details.

References

  1. Spider-Man (1962) https://en.wikipedia.org/wiki/With_great_power_comes_great_responsibility
  2. Rodrigo Ferreira and Moshe Vardi (2021) Deep Tech Ethics An Approach to Teaching Social Justice in Computer Science in Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE ’21), March 13–20, 2021, Virtual Event, USA. ACM, New York, NY, USA. DOI:10.1145/3408877.3432449
  3. Jack Swigert (1970) https://en.wikipedia.org/wiki/Houston,_we_have_a_problem

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

Join us to discuss ungraded assessment on Monday 4th January at 2pm GMT

Image via Good Ware and monkik edited by Bruce The Deus, CC BY-SA 4.0, via Wikimedia Commons w.wiki/qWo

The more time students spend thinking about their grades, the less time they spend thinking about their learning.

Ungraded (pass or fail) assessment provides an alternative to letter grading (A, B, C etc) which can address this issue. Join us on Monday 4th January at 2pm to discuss a new paper by David Malan which describes removing traditional letter grading from CS50: An introduction to Computer Science [1]. Heres is the abstract:

In 2010, we proposed to eliminate letter grades in CS50 at Harvard University in favor of Satisfactory / Unsatisfactory (SAT / UNS), whereby students would instead receive at term’s end a grade of SAT in lieu of A through C- or UNS in lieu of D+ through E. Albeit designed to empower students without prior background to explore an area beyond their comfort zone without fear of failure, that proposal initially failed. Not only were some concentrations on campus unwilling to grant credit for SAT, the university’s program in general education (of which CS50 was part) required that all courses be taken for letter grades.

In 2013, we instead proposed, this time successfully, to allow students to take CS50 either for a letter grade or SAT/UNS. And in 2017, we made SAT/UNS the course’s default, though students could still opt out. The percentage of students taking the course SAT/UNS jumped that year to 31%, up from 9% in the year prior, with as many as 86 of the course’s 671 students (13%) reporting that they enrolled because of SAT/UNS. The percentage of women in the course also increased to 44%, a 29-year high. And 19% of students who took the course SAT/UNS subsequently reported that their concentration would be or might be CS. Despite concerns to the contrary, students taking the course SAT/UNS reported spending not less but more time on the course each week than letter-graded classmates. And, once we accounted for prior background, they performed nearly the same.

We present the challenges and results of this 10-year initiative. We argue ultimately in favor of SAT/UNS, provided students must still meet all expectations, including all work submitted, in order to be eligible for SAT.

As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details.

References

  1. David Malan (2021) Toward an Ungraded CS50. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE ’21), March 13–20, 2021, Virtual Event, USA. ACM, New York, NY, USA. DOI:10.1145/3408877.3432461

Join us to discuss peer instruction on Monday 7th December at 2pm GMT

Peer instruction is a tried and tested technique for teaching popularised by the Harvard physicist Eric Mazur. Join us to discuss the use of peer instruction in introductory computing via a paper by Leo Porter and his collaborators, [1] which won an award from the ACM SIGCSE Technical Symposium Top Ten Papers of All Time. Here is the abstract:

Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. Over the past five years, there have been a number of research articles regarding the value of PI in computer science. The present work adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, we provide evidence that introductory computing instructors can successfully implement PI in their classrooms. We find encouraging minimum (74%) and average (92%) levels of success as measured through student valuation of PI for their learning. This work also documents and hypothesizes reasons for comparatively poor survey results in one course, highlighting the importance of the choice of grading policy (participation vs. correctness) for new PI adopters.

As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details and meeting URLs.

References

  1.  Porter, Leo; Bouvier, Dennis; Cutts, Quintin; Grissom, Scott; Lee, Cynthia; McCartney, Robert; Zingaro, Daniel; Simon, Beth (2016). “A Multi-institutional Study of Peer Instruction in Introductory Computing”: SIGCSE ’16: Proceedings of the 47th ACM Technical Symposium on Computing Science Education 358–363. DOI:10.1145/2839509.2844642.

Join us to discuss student misconceptions in programming, March 23rd from 1pm to 2pm

The Scream by Edvard Munch 😱, reproduced in LEGO by Nathan Sawaya, the BrickArtist.com

Join us to discuss Identifying Student Misconceptions of Programming by Lisa Kaczmarczyk et al [1] which was voted a top paper from the last 50 years by SIGCSE members in 2019. Here is a summary:

Computing educators are often baffled by the misconceptions that their CS1 students hold. We need to understand these misconceptions more clearly in order to help students form correct conceptions. This paper describes one stage in the development of a concept inventory for Computing Fundamentals: investigation of student misconceptions in a series of core CS1 topics previously identified as both important and difficult. Formal interviews with students revealed four distinct themes, each containing many interesting misconceptions. Three of those misconceptions are detailed in this paper: two misconceptions about memory models, and data assignment when primitives are declared. Individual misconceptions are related, but vary widely, thus providing excellent material to use in the development of the CI. In addition, CS1 instructors are provided immediate usable material for helping their students understand some difficult introductory concepts.

In case you’re wondering, CS1 refers to the first course in the introductory sequence of a computer science major (in American parlance), roughly equivalent to first year undergraduate in the UK. CI refers to a Concept Inventory, a test designed to tell teachers exactly what students know and don’t know. According to Reinventing Nerds, the paper has been influential because it was the “first to apply rigorous research methods to investigating misconceptions”.

References

  1. Kaczmarczyk, Lisa C.; Petrick, Elizabeth R.; East, J. Philip; Herman, Geoffrey L. (2010). Identifying student misconceptions of programmingSIGCSE ’10: Proceedings of the 41st ACM technical symposium on Computer science education, pages 107–111. doi:10.1145/1734263.1734299