O'Really?

July 28, 2022

What’s your story, coding glory?

Filed under: Uncategorized — Duncan Hull @ 11:21 am
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Congratulations to all this years graduates!

Last week we celebrated graduation, its been the first proper graduation since before the pandemic. A lot proverbial water has passed very quickly under our proverbial bridge since this years graduates starting studying back in 2018/19. What obstacles have they faced during their study and placements and how have they overcome them? Where are they going next? What’s their story? I interviewed five of this years graduands and previous years graduates to find out. Hear from some of our students including:

  • Sneha Kandane, she’s returning Matillion where she did her industrial placement cdyf.me/sneha
  • Carmen who did an internship at McKinsey and a placement at The Walt Disney Company cdyf.me/carmen
  • Brian Yim Tam who did a placement at Disney Streaming here in Manchester cdyf.me/brian
  • Raluca Cruceru who did a placement at CERN where she now works as a software engineer cdyf.me/raluca
  • Jason Ozuzu who did a placement at Morgan Stanley, an internship at FitBit and is joining Google in London cdyf.me/jason

Listen online at Coding your Future or subscribe wherever you get your podcasts cdyf.me/hearing#subscribing

Congratulations to all this years graduates, it was lovely to celebrate your achievements despite the considerable challenges you’ve faced during the last three of four years. Thanks to Sneha, Carmen, Brian, Raluca and Jason for sharing your stories too.

May 25, 2022

Join us to discuss teaching programming to Physics students on Monday 13th June at 2pm BST

Filed under: Uncategorized — Duncan Hull @ 10:14 am
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CC BY-SA image of Bohr model of the atom by Jabberwock on Wikimedia Commons w.wiki/59id 

print(’Hello World!’) is all very well but it doesn’t help physics students solve the Schrödinger equation. Join us for our next journal club meeting on Monday 13th June at 2pm BST where we’ll be discussing a paper by Lloyd Cawthorne on teaching programming to undergraduate Physics students. From the abstract:

Computer programming is a key component of any physical science or engineering degree and is a skill sought by employers. Coding can be very appealing to these students as it is logical and another setting where they can solve problems. However, many students can often be reluctant to engage with the material as it might not interest them or they might not see how it applies to their wider study. Here, I present lessons I have learned and recommendations to increase participation in programming courses for students majoring in the physical sciences or engineering. The discussion and examples are taken from my second-year core undergraduate physics module, Introduction to Programming for Physicists, taught at The University of Manchester, UK. Teaching this course, I have developed successful solutions that can be applied to undergraduate STEM courses.

All welcome. As usual we’ll be meeting on zoom, details are in the slack channel sigcse.cs.manchester.ac.uk/join-us.

References

  1. Lloyd Cawthorne (2021) Invited viewpoint: teaching programming to students in physical sciences and engineering, Journal of Materials Science 56, pages 16183–16194 DOI:10.1007/s10853-021-06368-1

March 7, 2022

Join us to discuss the feeling of learning ❤️ (vs. actual learning) on Monday 4th April at 2pm BST

Filed under: Uncategorized — Duncan Hull @ 4:08 pm

Learning can be an emotional process and we often don’t realise when we are actually learning. When you’re listening to an expert explain something well, it’s easy to mistake the speaker’s smooth delivery for your own understanding. You might feel like you’re learning, but actual learning is often hard work and feels uncomfortable. Join us to discuss actual learning vs. feeling of learning via a paper by Louis Deslauriers, Logan S. McCarty, Kelly Miller, Kristina Callaghan, and Greg Kestin at Harvard University here is the abstract:

We compared students’ self-reported perception of learning with their actual learning under controlled conditions in large-enrollment introductory college physics courses taught using 1) active instruction (following best practices in the discipline) and 2) passive instruction (lectures by experienced and highly rated instructors). Both groups received identical class content and handouts, students were randomly assigned, and the instructor made no effort to persuade students of the benefit of either method. Students in active classrooms learned more (as would be expected based on prior research), but their perception of learning, while positive, was lower than that of their peers in passive environments. This suggests that attempts to evaluate instruction based on students’ perceptions of learning could inadvertently promote inferior (passive) pedagogical methods. For instance, a superstar lecturer could create such a positive feeling of learning that students would choose those lectures over active learning. Most importantly, these results suggest that when students experience the increased cognitive effort associated with active learning, they initially take that effort to signify poorer learning. That disconnect may have a detrimental effect on students’ motivation, engagement, and ability to self-regulate their own learning. Although students can, on their own, discover the increased value of being actively engaged during a semester-long course, their learning may be impaired during the initial part of the course. We discuss strategies that instructors can use, early in the semester, to improve students’ response to being actively engaged in the classroom..

see [1]

Thanks to Uli Sattler and Andrea Schalk for highlighting the paper. All welcome. As usual we’ll be meeting on zoom, details are in the slack channel sigcse.cs.manchester.ac.uk/join-us.

References

  1. Logan S. McCarty; Kelly Miller; Kristina Callaghan; Greg Kestin (2019) “Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom”Proceedings of the National Academy of Sciences of the United States of America: 201821936. DOI:10.1073/PNAS.1821936116 PMC: 6765278 PMID: 31484770
  2. Jill Barshay (2022) College students often don’t know when they’re learning: Harvard experiment reveals the psychological grip of lectures, The Hechinger Report

March 1, 2022

Join us to discuss conversational programming on Monday 7th March at 2pm GMT

Between the traditional division of non-programmers and programmers, there is a third category of conversational programmers. These are people who learn programming so that they can speak in the “programmer’s language” in order to collaborate better with software engineers. Join us to discuss conversational programming via paper by Katie Cunningham et al. [1] This won a best paper award at SIGCSE 2022:

As the number of conversational programmers grows, computing educators are increasingly tasked with a paradox: to teach programming to people who want to communicate effectively about the internals of software, but not write code themselves. Designing instruction for conversational programmers is particularly challenging because their learning goals are not well understood, and few strategies exist for teaching to their needs. To address these gaps, we analyse the research on programming learning goals of conversational programmers from survey and interview studies of this population. We identify a major theme from these learners’ goals: they often involve making connections between code’s real-world purpose and various internal elements of software. To better understand the knowledge and skills conversational programmers require, we apply the Structure Behaviour Function framework to compare their learning goals to those of aspiring professional developers. Finally, we argue that instructional strategies for conversational programmers require a focus on high-level program behaviour that is not typically supported in introductory programming courses.

See [1] below

All welcome. As usual we’ll be meeting on zoom, details are in the slack channel sigcse.cs.manchester.ac.uk/join-us.

References

  1. Kathryn Cunningham, Yike Qiao, Alex Feng and Eleanor O’Rourke (2022) Bringing “High-level” Down to Earth: Gaining Clarity in Conversational Programmer Learning Goals in SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, Pages 551–557 DOI:10.1145/3478431.3499370

December 23, 2021

Join us virtually in Durham to discuss Computing Education Practice (CEP) on 6th Jan 2022

Filed under: education,Uncategorized — Duncan Hull @ 10:40 am
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Picture of Durham Cathedral by Mattbuck on Wikimedia Commons w.wiki/4acc

The ACM Computing Education Practice (CEP) conference is aimed at practitioners and researchers in computing education, both within Computer Science departments and elsewhere. The conference provides a platform to share and discuss innovations and developments in the practice of computing education. CEP is a community, not just a series of proceedings; everybody is encouraged to participate even if they are not presenting. We have an exciting programme of talks and workshops scheduled which will be of interest to anyone teaching Computer Science including:

  • Narrowing and Stretching: Addressing the Challenges of Multi-track programming by Steven Bradley and Eleni Akrida, Durham University
  • Automated Code Tracing Exercises for CS1 by Sean Russell, University College Dublin
  • Feedback and Engagement on an Introductory Programming Module by Beate Grawemeyer et al Coventry University 
  • Gender parity in peer assessment of team software development projects by Tom Crick et al Swansea University
  • Promoting Engagement in Remote Computing Ethics Education by Joseph Maguire and Steve Draper, University of Glasgow
  • Co-constructing a Community of Practice for Early-Career Computer Science Academics in the UK by Tom Crick et al 
  • Assessing Knowledge and Skills in Forensics with Alternative Assessment Pathways by Joseph Maguire
  • Little Man Computer + Scratch: A recipe to construct a mental model of program execution by Noman Javed, London School of Economics
  • Application of Amazon Web Services within teaching & learning at a UK University by Dan Flood Coventry University

The conference will be held online on Thursday 6th January 2022. More info and registration at cepconference.webspace.durham.ac.uk/programme. We look forward to seeing you there. 

On behalf of the UK ACM Special Interest Group on Computer Science Education (SIGCSE) uki-sigcse.acm.org/about/

July 30, 2021

Join us to discuss when study turns digital on Monday 2nd August at 2pm BST

Public domain image of Coronavirus by Alissa Eckert and Dan Higgins at CDC.gov on Wikimedia commons w.wiki/ycs

The pandemic has accelerated changes to the way we teach and learn. Join us to discuss the Covid-19 shutdown: when studying turns digital, students want more structure: a paper by Vegard Gjerde, Robert Gray, Bodil Holst and Stein Dankert Kolstø on the effects of the pandemic on Physics Education at a Norwegian University. [1]

In March 2020, universities in Norway and many other countries shut down due to the Covid-19 pandemic. The students lost access to classrooms, libraries, study halls, and laboratories. Studying turned digital. Because it is unclear when this pandemic will cease to affect students and because we cannot know whether or when a new pandemic occurs, we need to find ways to improve digital study-life for students. An important step in this direction is to understand the students’ experiences and perspectives regarding how the digitalization affected their study-life both in structured learning arenas and their self-study. Therefore, we interviewed 12 students in an introductory mechanics course at a Norwegian university in June of 2020. Through a thematic analysis, we identified four broad categories in the students’ different experiences and reflections, namely that digitalization: (a) provides benefits, e.g. the flexibility inherent in online video lectures; (b) incurs learning costs, e.g. students reducing their study effort; (c) incurs social costs, e.g. missing being around other students; and (d) increases the need for structure, e.g. wanting to be arranged in digital groups to solve mandatory tasks. We also found that the 2019 students on average scored significantly better on the final exam than the 2020 students, d = 0.31, but we discuss why this result should be interpreted with caution. We provide suggestions for how to adapt courses to make students’ digital studying more socially stimulating and effective. Furthermore, this study is a contribution to the historical documentation of the Covid-19 pandemic.

All welcome, as usual, we’ll be meeting on Zoom see sigcse.cs.manchester.ac.uk/join-us for details. Thanks to Sarah Clinch for suggesting the paper.

References

  1. Gjerde, Vegard; Gray, Robert; Holst, Bodil; Kolstø, Stein Dankert (2021). “The Covid-19 shutdown: when studying turns digital, students want more structure”. Physics Education56 (5): 055004. doi:10.1088/1361-6552/ac031e

July 5, 2021

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

Filed under: Teaching,Uncategorized — Duncan Hull @ 11:56 am
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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

March 23, 2021

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

Scientist icon made by Eucalyp via 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

May 26, 2020

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

Filed under: education,Uncategorized — Duncan Hull @ 2:41 pm
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7704609288_14d335dd87_cAt our next journal club, on Monday 1st June at 11am, we’ll be discussing blended learning. We’ve picked a subject, but we haven’t picked a paper yet. So, if you know interesting papers on blended learning, post them to the “paper suggestions” channel at  uk-acm-sigsce.slack.com – there are a few suggestions up there already for starters.

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!

April 28, 2020

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

Filed under: education,engineering,Uncategorized — Duncan Hull @ 10:17 am
Tags: , , , ,

Hieroglyphs_from_the_tomb_of_Seti_I

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? We’ll be meeting on Zoom, details will be sent to anyone who registers at sigman2.eventbrite.co.uk

References

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

 

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