Join us to discuss blended learning & pedagogy in Computer Science on Monday 6th July at 3pm

What is innovative pedagogy? Picture by Giulia Forsythe

Join us for our next journal club meeting on Monday 6th July at 3pm, the papers we’ll be discussing below come from the #paper-suggestions channel of our slack workspace at

Show me the pedagogy!

The first paper is a short chapter by Katrina Falkner and Judy Sheard which gives an overview of pedagogic approaches including active learning, collaborative learning, cooperative learning, contributing student pedagogy (CSP), blended learning and MOOCs. [1] This was published last year as chapter 15 of the Cambridge Handbook on Computing Education Research edited by Sally Fincher and Anthony V. Robins. A lot of blended learning resources focus on technology, this chapter talks about where blended learning fits with a range of different pedagogic approaches.

A video summary of all sixteen chapters of the Cambridge Handbook of Computing Education Research, including chapter 15 which we’ll be discussing

Implementing blended learning

The second paper (suggested by Jane Waite) is Design and implementation factors in blended synchronous learning environments [2], here’s a summary from the abstract:

Increasingly, universities are using technology to provide students with more flexible modes of participation. This article presents a cross-case analysis of blended synchronous learning environments—contexts where remote students participated in face-to-face classes through the use of rich-media synchronous technologies such as video conferencing, web conferencing, and virtual worlds. The study examined how design and implementation factors influenced student learning activity and perceived learning outcomes, drawing on a synthesis of student, teacher, and researcher observations collected before, during, and after blended synchronous learning lessons. Key findings include the importance of designing for active learning, the need to select and utilise technologies appropriately to meet communicative requirements, varying degrees of co-presence depending on technological and human factors, and heightened cognitive load. Pedagogical, technological, and logistical implications are presented in the form of a Blended Synchronous Learning Design Framework that is grounded in the results of the study.

Hope to see you there, zoom details are on the slack channel, email me if you’d like to request an invitation to the slack channel. Likewise, if you don’t have access to the papers let me know.


  1.  Falkner, Katrina; Sheard, Judy (2019). “Pedagogic Approaches”: 445–480. doi:10.1017/9781108654555.016. Chapter 15 of the The Cambridge Handbook of Computing Education Research
  2. ^ Bower, Matt; Dalgarno, Barney; Kennedy, Gregor E.; Lee, Mark J.W.; Kenney, Jacqueline (2015). “Design and implementation factors in blended synchronous learning environments: Outcomes from a cross-case analysis”. Computers & Education86: 1–17. doi:10.1016/j.compedu.2015.03.006ISSN 0360-1315.

Join us to discuss blended learning, Monday 1st June at 11am on Zoom

Blended learning image via Giulia Forsythe

At our next journal club, on Monday 1st June at 11am, we’ll be discussing blended learning. We’ve picked a paper from “paper suggestions” channel at The paper is Preparing for the Digital University, a review commissioned by the Gates Foundation.

We’ll be watching this short video intro to blended learning

If you’ve not got access to the workspace yet, ping me or Alcywn Parker and we’ll add you to the group. Journal Club is part of the Association for Computing Machinery (ACM) Special Interest Group (SIG) on Computer Science Education (CSE) – all welcome!

Join us to discuss learning programming languages: Monday 4th May at 11am #sigcsejclub

Hieroglyphs from the tomb of Seti I, by Jon Bodsworth via Wikimedia Commons and the Egypt archive

ACM SIGCSE Journal Club returns Monday 4th May at 11am. The paper we’re discussing this month is “Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages” by Chantel Prat et al published in Scientific Reports. [1] Here’s the abstract:

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second “natural” language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors. Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50–72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments

The paper describes an experiment which investigates the relationship between learning natural languages and programming languages and draws some interesting conclusions that provide some good discussion points. Does being good at learning natural languages like English make you good at learning programming language like Python? Do linguists make good coders?


  1. Prat, C.S., Madhyastha, T.M., Mottarella, M.J. et al. (2020) Relating Natural Language Aptitude to Individual Differences in Learning Programming LanguagesScientific Reports 10, 3817 (2020). DOI:10.1038/s41598-020-60661-8

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

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”.


  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