Rather than meeting online in January, we’ll be meeting in person. So join us at Durham University for the annual Computing Education Practice (CEP) conference which takes place on Friday 5th January, with a pre-conference dinner in the evening of Thursday 4th January.
It’s all very well getting an AI to write your code for you but neither writing code or reading code are the same as understanding code. So what is going on in novices brains when they learn to actually understand the code they are reading and writing? Join us on Monday 6th March at 2pm GMT to discuss a paper by Quintin Cutts and Maria Kallia from the University of Glasgow on this very topic , from the abstract:
An approach to code comprehension in an introductory programming class is presented, drawing on the Text Surface, Functional and Machine aspects of Schulte’s Block Model, and emphasising programming as a modelling activity involving problem and machine domains. To visually connect the domains and a program, a key diagram conceptualising the three aspects lies at the approach’s heart, alongside instructional exposition and exercises, which are all presented. Students find the approach challenging initially, but most recognise its value later, and identify, unexpectedly, the value of the approach for problem decomposition, planning and coding.
We’ll be joined by one of the co-authors (Quintin Cutts), who’ll give us a lightning talk summary of the paper to kick-off our journal club discussion.  Quintin has added: “You can’t write if you can’t read. In just four pages the paper outlines a classroom approach to developing in novices good code comprehension right from the start of an introductory course. There’s also some feedback on what students thought, a year later – spoiler – they seemed to get a lot from it. Anyone teaching introductory programming might find such a short paper thought provoking, even if they don’t pick up the technique in their teaching. Worth a quick read, and coming along to listen/add to the discussion…”
Quintin Cutts and Maria Kallia (2023) Introducing Modelling and Code Comprehension from the First Days of an Introductory Programming Class in CEP ’23: Proceedings of 7th Conference on Computing Education Practice Pages 21–24 DOI:10.1145/3573260.3573266
Spatial skills can be beneficial in engineering and computing, but how are they connected? Why are spatial abilities beneficial in engineering? Join us to discuss this via a paper on spatial skills training by Jack Parkinson and friends at the University of Glasgow. Here is the abstract:
We have been training spatial skills for Computing Science students over several years with positive results, both in terms of the students’ spatial skills and their CS outcomes. The delivery and structure of the training has been modified over time and carried out at several institutions, resulting in variations across each intervention. This article describes six distinct case studies of training deliveries, highlighting the main challenges faced and some important takeaways. Our goal is to provide useful guidance based on our varied experience for any practitioner considering the adoption of spatial skills training for their students.
Jack Parkinson, Ryan Bockmon, Quintin Cutts, Michael Liut, Andrew Petersen and Sheryl Sorby (2021) Practice report: six studies of spatial skills training in introductory computer science, ACM Inroads Volume 12, issue 4, pp 18–29 DOI: 10.1145/3494574
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  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.
All welcome. As usual, we’ll be meeting on zoom. Thanks to Steven Bradley for suggesting this months paper.
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
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.
Minimal guidance is a popular approach to teaching and learning. This technique advocates teachers taking a back seat to facilitate learning by letting their students get on with it. Minimal guidance comes in many guises including constructivism, discovery learning, problem-based learning, experiential learning, active learning, inquiry-based learning and even lazy teaching. According to its critics, unguided and minimally guided approaches don’t work. Join us to discuss why via a paper  published by Paul Kirschner, John Sweller and Richard Clark, here is the abstract:
Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert–novice differences, and cognitive load. Although unguided or minimally guided instructional approaches are very popular and intuitively appealing, the point is made that these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half-century that consistently indicate that minimally guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide “internal” guidance. Recent developments in instructional research and instructional design models that support guidance during instruction are briefly described.
Kirschner, Paul A.; Sweller, John; Clark, Richard E. (2006). “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching”. Educational Psychologist. 41 (2): 75–86. DOI: 10.1207/s15326985ep4102_1 (see also altmetric.com/details/564640 for online attention scores)