O'Really?

November 2, 2020

Join us to discuss why minimal guidance doesn’t work on Monday 2nd November at 2pm GMT

Photo via NESA by Makers on Unsplash

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 [1] published by Paul KirschnerJohn 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.

This is a controversial, heavily cited and politically motivated paper which has provoked numerous rebuttals, making it an ideal candidate for a juicy journal club discussion!

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

References

  1. 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

July 27, 2020

Join us to discuss how video production affects student engagement Monday 3rd August at 11am

The MOOC! the movie image by Giulia Forsythe image published CC-BY-NC-SA

As Universities transition to online teaching during the global coronavirus pandemic, there’s increasing interest in the use of pre-recorded videos to replace traditional lectures in higher education. Join us to discuss how video production affects student engagement, based on a paper published by Philip Guo at the University of California, San Deigo (UCSD) from the Learning at Scale conference on How video production affects student engagement: an empirical study of MOOC videos. (MOOC stands for Massive Open Online Course). [1] Here is the abstract:

Videos are a widely-used kind of resource for online learning. This paper presents an empirical study of how video production decisions affect student engagement in online educational videos. To our knowledge, ours is the largest-scale study of video engagement to date, using data from 6.9 million video watching sessions across four courses on the edX MOOC platform. We measure engagement by how long students are watching each video, and whether they attempt to answer post-video assessment problems.

Our main findings are that shorter videos are much more engaging, that informal talking-head videos are more engaging, that Khan-style tablet drawings are more engaging, that even high-quality pre-recorded classroom lectures might not make for engaging online videos, and that students engage differently with lecture and tutorial videos.

Based upon these quantitative findings and qualitative insights from interviews with edX staff, we developed a set of recommendations to help instructors and video producers take better advantage of the online video format. Finally, to enable researchers to reproduce and build upon our findings, we have made our anonymized video watching data set and analysis scripts public. To our knowledge, ours is one of the first public data sets on MOOC resource usage.

Details of the zoom meeting will be posted on our slack workspace at uk-acm-sigsce.slack.com. If you don’t have access to the workspace, send me (Duncan Hull) an email to request an invite to join the workspace. The paper refers to several styles of video production, some examples below.

Khan style tablet drawings

The paper refers to Khan style videos, this is an example, taken from Khan Academy course on algorithms, khanacademy.org/computing/computer-science/algorithms

What is an algorithm? Video introduction to Khan Academy algorithms course by Thomas Cormen and Devin Balkcom

Talking Heads

Some examples of “talking head” videos:

How to frame a talking head with Tomás De Matteis

There’s more than one way to do talking head videos, see Moving to Blended Learning, Part 3: Types of Video at www.elearning.fse.manchester.ac.uk/fseta/moving-to-blended-learning-part-3-types-of-video/

Making video-friendly slides

My colleague Steve Pettifer explains how to make video-friendly slides

Lose the words! Your PowerPoint / Keynote presentation should not be a script or a handout

References

  1. Guo, Philip J.; Kim, Juho; Rubin, Rob (2014). “How video production affects student engagement“. Proceedings of the first ACM conference on Learning @ scale conference: 41–50. doi:10.1145/2556325.2566239.

July 2, 2020

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

What is innovative pedagogy? CC licensed image by @giuliaforsythe

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 uk-acm-sigsce.slack.com.

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.

References

  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.

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
Tags: ,

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

 

March 4, 2020

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

smallerscream

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

In Canterbury, Glasgow and Manchester, we’re starting a journal club, as part of uki-sigcse.acm.org, the Association for Computing Machinery (ACM) Special Interest Group (SIG) on Computer Science Education (CSE). Journal clubs are like a book clubs, but instead of chatting about books we discuss journal papers instead. Who should come? What’s on the agenda? How can you join and what are our club rules? Read on…

Who should come?

Our journal club will be of interest to:

  • Educators who teach some flavour of computing or you run a coding boot camp.
  • Employers who employ and train software engineers, data scientists, developers, coders, programmers, etc
  • Employees your boss has sent you on a training program or bootcamp to learn or improve your programming
  • Students what misconceptions about programming have you encountered?
  • Everyone and anyone who is curious. Our doors are open, this is not an ivory tower. Everyone has something to learn, everyone has something to teach.

Agenda: The paper we’ll be discussing

If you’d like to join us, read the paper: 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”. After a brief introduction to the paper and its authors we will discuss the following:

  • What is good about the paper?
  • What could be improved?
  • What is the most surprising or interesting thing you got from the paper?
  • How convincing is the evidence, arguments and conclusions presented?
  • How could you use the results and insights in your own teaching or training program?
  • What are the next steps that follow on from this research? What has already been done to follow on from this work?
  • Has consensus and opinion moved since the publication of this paper ten years ago? If so, how and why?
  • Why was this paper voted top 10 of all time by SIGCSE.org members?
  • Are there any elephants in the room? Does the paper omit anything relevant or gloss over important details?
  • What do we know that we know (Rumsfeld’s known knowns)
  • What do we know that we don’t know (Rumsfeld’s known unknowns)
  • A.O.B.: Any other questions or comments?
  • Why was this paper chosen for journal club?
  • What paper should we discuss at our next meeting?

How can you join?

We’ll be meeting in the Atlas rooms, Kilburn building, Department of Computer Science, University of Manchester, M13 9PL, see bit.ly/directions-to-kilburn-building and www.cs.manchester.ac.uk/about/maps-and-travel online using Zoom, find login details and register at sigman1.eventbrite.co.uk.

Can’t make it this time? Groups will be running in parallel in Glasgow (23rd March at 1pm with Quintin Cutts) and Canterbury (Friday 27th March, 14.00, Room S132 in the Cornwallis building, School of Computing with Sally Fincher) to discuss the same paper. You can also join us online using the hashtag #SIGCSEJClub. If you’d like to know about future journal clubs in Manchester send an email to with the text…

subscribe sigcse-journal-club yourfirstname yoursecondname

…in the body of your email.

Start your own local journal club

If Manchester, Glasgow or Canterbury aren’t easy for you to get to, start your own journal club by joining SIGCSE at uki-sigcse.acm.org/membership and posting the details to their mailing list. We plan to have regular journal clubs every three months or so where we’ll discuss the same paper nationally during journal club week: this one is Monday 23rd to Friday 27th March.

 

Journal club rules

We will loosely be following the guidelines at Ten Simple Rules for Running a Journal Club including:

  • It will be casual  not formal. There will be coffee and refreshments available. We won’t be providing lunch but feel free to bring your own. Some companies call them brown bag meetings, because many of us may will only have an hour so we need to get straight down to business.
  • It’s about more than just the articles. We are building (and strengthening) communities of practice amongst peers in Computer Science education, not just inside academia but in industry as well. Don’t be shy, all are welcome!
  • Multidisciplinary is not a dirty word: we aim to foster equality, diversity and inclusion of different people, disciplines, practices and viewpoints. That means we’re open to anyone teaching computer science. That could be in a school, FE college, University, bootcamp, onboarding scheme, company induction or employers staff training program etc. Students are welcome too. The more diverse our journal club is, the stronger it will be.
  • Topics will reflect the diversity of our membership. We’ve started with student misconceptions, but we invite proposals for which paper we should discuss at our next meeting so we can vote on them.
  • We’ll pick interesting papers, but they don’t have to be award winning. Papers don’t need to be heavily cited either, but they do have to be thought provoking and provide something meaty to discuss alongside practical tips that can be put into practice straight away.

Any questions? Let me know in the comments section below, via email or twitter.

You might also like…

If you care about the training & education of software engineers and computer scientists, you might also be interested in #CSEdResearchBookClub which will take place on Thursday 5th March at 8pm. They’ll be discussing a paper by Sue Sentance et al. on using Predict, Run, Investigate, Modify & Make (PRIMM) called Teaching computer programming with PRIMM: a sociocultural perspective. CS education book club is co-ordinated by Jane Waite at Queen Mary University of London (QMUL) see below:

References

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

January 27, 2020

Seven things to do at CERN if you’re not a Physicist

cern

Wandering the Immeasurable: A sculpture at CERN by Gayle Hermick, picture re-used with permission from the artist

Even if you’re not a Physicist, there is plenty to see and do above and below ground at the European Organization for Nuclear Research (CERN). Home to the worlds largest experiment on what is arguably the worlds largest machine near Geneva in Switzerland, CERN is a very inspiring place to visit. Consequently, CERN and the Large Hadron Collider (LHC) feature in many guidebooks like The Geek Atlas [1], the Atlas Obscura, Lonely Planet and Tripadvisor.com. So what can you actually see and do at CERN?

  1. Get a well paid engineering job. Good news for engineers, there are loads of jobs at CERN. What better way to explore a place than to work there? If you’re a student see careers.cern/students for details on summer internships and year long technical student programs. If you have already graduated, take a look at the CERN Fellowships and the doctoral student program. There are also plenty of opportunities for more experienced engineers described at careers.cern/professionals too. CERN’s mission is to “unite people from all over the world to push the frontiers of science and technology, for the benefit of all”. Part of that means providing opportunities for people from CERN’s 23 member states to learn new skills at CERN and take them back to their home country. For every research physicist at CERN, there are ten engineers. [2] To run their experiments, physicists rely on massive, novel and a very precise network of machines made with millions of parts, both moving and stationary. You need an army of engineers to build, test, run and develop such a complex machine, for example:
    • Mechanical engineers develop heating & cooling systems and mechatronics (there are quite a few robots at CERN)
    • Materials engineers test novel materials, metals, magnets, microscopes, superconductors, vacuums, X-ray diffraction and apply radiochemistry
    • Software and hardware engineers develop applications, virtualised infrastructure, distributed computing and databases using a wide range of programming and scripting languages. These applications manage data in one of the most highly demanding computing environments in the research world
    • Electrical and electronic engineers work on energy distribution, signal processing, microelectronics and radio frequency technology
    • Civil engineers and geotechnical engineers develop structures, roads, drainage, both above (and under) ground to accommodate all of the above
    • There are non-engineering jobs too, in administration careers.cern/AdminStudent-projects and Applied Physics (obviously)

So CERN is full of engineers of every flavour. But if you’re not a physicist or an engineer looking for a job, there is still plenty to see and do. So let’s reboot our listicle again: seven things to do at CERN if you’re not a physicist, an engineer or job seeker:

  1. Watch cosmic rays arrive from outer space: There are two permanent exhibitions which can be visited without booking and they both have free entry. One is housed in the aesthetically pleasing Globe of Science and Innovation (GoSI) and is called the Universe of Particles. Another is opposite the GoSI and called Microcosm. There’s plenty to see in both exhibits, including film projections, spark chambers showing cosmic rays and cloud chambers which allow you to visualise ionizing radiation.
  2. Wander the Immeasurable with Gayle Hermick: Right outside the GoSI, sits an impressive sculpture made of 15 tonnes of twisted steel, stretched out over 37 metres in length and 11 metres up into the air. Covered in mathematical equations describing physical laws, the sculpture tells the story of Physics from Mesopotamia and Ancient Greece up to present day Higgs Boson and beyond. It’s a beautiful work of art to contemplate by Gayle Hermick. Having been inspired by equations the next thing you need to do is…
  3. Crunch numbers using Einsteins famous equation: You can’t visit CERN without crunching some numbers. Many people will be familiar with Einsteins famous equation of mass–energy equivalence E=mc². What this means is that energy can be converted into mass (and vice versa) and the “exchange rate” () is a very large number – the speed of light squared. So, you can turn a small about of mass into a HUGE amount of energy. Armed with your handy mass–energy calculator, you can crunch numbers, for example 1 kg = 90,000,000,000,000,000 Joules.
  4. Thank the technology mothership: CERN is widely known as the the birthplace the Web, which we should all be thankful for. Many other technologies can trace their origin to CERN. Bent Stumpe and his colleagues developed the first touchscreens as early as 1973. [3,4] Cloud computing platforms such as Amazon Web Services, Google Cloud, Microsoft Azure have some of their roots in Grid Computing developed at CERN too. [5] Key pieces of widely used open-source software like Ceph and OpenStack have been co-developed at CERN. Where would we be without massive international collaborations? Find out more about how investment creates a positive impact on society through knowledge transfer, spin outs, startups and more at kt.cern. Many of these projects have an impact far beyond physics in areas such as medicine and consumer electronics. Thank you technology mothership. 🙏
  5. Boggle at Big Data: Data speaks louder than words. Here is some random data for your mind to boggle on:
    • When switched on, some of the LHC detectors track up to 40 million events per second.
    • The LHC Grid computing generates 30 petabytes (10¹⁵ bytes) per year, with 300 petabytes of data permanently archived in its tape libraries as of October 2018.
    • The big loop underground is 27km long. Travelling very fast, close to the speed of light, a proton laps the circuit 11,000 times every second.
    • There are 100,000 scientists from over 100 countries working at CERN
    • More boggling can be done in the CERN data centre, especially the key facts and figures. [6] Anyone can explore and play with over two petabytes of Physics data at opendata.cern.ch
  6. Contribute to the Grid: Talking of data, Physicists from all over the world work on data produced by the experiments. This requires supercomputers, very High Performance Computing (HPC) and Grid computing that no single machine can provide. This is why the Worldwide LHC Computing Grid (WLCG) exists. With the improvements of the LHC more and more computing power is required to crunch the data. Anyone can contribute by joining in the LHC@home project. Who knows? Maybe you can be a part of the discovery of the new mysterious particle or the proof that physicists have been struggling with for decades. CERN’s Grid builds on volunteered resources provided via the Berkeley Open Infrastructure for Network Computing (BOINC) middleware.
  7. Book a free tour: While the two free permanent exhibitions require no booking, the free tours do and they offer much more. Tours are typically given by knowledgeable and enthusiastic staff. You can learn a lot from the permanent exhibitions, but a tour guide brings the place to life. Tours fill up quickly and provide access to restricted parts of CERN such as mission control, the ATLAS experiment, CMS cavern, synchro-cyclotron, the CERN data centre and more. [6] The cyclotron tells the story of CERN from 1957, when the first particle accelerator arrived in pieces on the back of a few lorries. Today it spans 27 km of France and Switzerland. How did that happen? Using lights and projectors, the exhibition brings the story to life in an illuminating way. At the time of writing, limited underground visits are possible as we are in the middle of the long shutdown 2 [7]. Tunnels are accessible but you’ll need to book a tour.

If you ever get the chance to visit.cern, it is well worth it. There is nowhere else quite like it. CERN is a truly inspiring place that demonstrates what can be achieved when thousands of people collaborate on a shared vision.

Acknowledgements

I’d like to thank current and former CERN technical students from the University of Manchester for their tours (both virtual and actual) of CERN and comments on drafts of this article: Raluca Cruceru, Simeon Tsvetankov, Iuliana Voinea, Grzegorz Jacenków, Boris Vasilev, Ciprian Tomoiagă, Nicole Morgan, Paul-Adrian Gafton, Joshua Dawes and Stefan Klikovits. Did I miss anything? Let me know in the comments or by email.

Thanks to Gayle Hermick for her permission to re-use the picture of her artwork in this piece.

DISCLAIMER: You can probably tell from reading the above that I am not a Physicist, unless you count a very rusty A-level from decades ago. Any factual errors in this article are the combined fault of me and my Physics teacher!

References

    1. John Graham-Cumming (2009) The Geek Atlas: 128 places where Science & Technology come alive O’Reilly Media, Inc. ISBN: 9780596802257
    2. Did you know, CERN employs ten times more engineers and technicians than research physicists? home.cern/science/engineering Deadlines for applications are typically, end of January for summer internships and September and March for technical studentships, check careers.cern for details.
    3. Bent Stumpe and Christine Sutton (2010) The first capacitative touch screens at CERN: The story of a forerunner to today’s mobile-phone screens, cerncourier.com
    4. Bent Stumpe (2014) The ‘Touch Screen’ Revolution: 103–116. DOI: 10.1002/9783527687039.ch05 Chapter 5 of From Physics to Daily Life by Beatrice Bressan Wiley‐VCH Verlag GmbH & Co ISBN: 9783527332861
    5. Maria Alandes Pradillo and Andrzej Nowak (2013) The Grid, CERN’s Global Supercomputer Computerphile
    6. Mélissa Gaillard (2019) Key Facts and Figures – CERN Data Centre information-technology.web.cern.ch
    7. Evan Gough (2018) The Large Hadron Collider has been Shut Down, and Will Stay Down for Two Years While they Perform Major Upgrades universetoday.com

 

December 10, 2019

Thank you Sara and Bhav at Wikimedia UK

320px-TtT_Group_Shot_2

Participants in the Training of Trainers workshop at the University of Glasgow, November 2019. Picture by Sara Thomas (WMUK) [CC BY-SA 4.0 commons.wikimedia.org/wiki/File:TtT_Group_Shot_2.jpg

Last month I attended a three day Training of Trainers (ToT) course at the University of Glasgow. Run as an interactive workshop, the course was designed to help leaders of Wikipedia training events to improve their delivery and organisation. Having participated and run several Wikipedia events in the past, such an Ada Lovelace event earlier this year, I was keen to learn how do things better. Here’s a report on the workshop, with some bonus extra curricular Glasgow goodies thrown in for good measure. Thanks again Sara Thomas and Bhavesh Patel for organising and delivering the course.

As a charity, Wikimedia UK (WMUK) is part of the global Wikimedia movement. WMUK organises events in order to:

… work in partnership with organisations from the cultural and education sectors and beyond in order to unlock content, remove barriers to knowledge, develop new ways of engaging with the public and enable learners to benefit fully from the educational potential of the Wikimedia projects.

Most of the workshop participants (pictured top right) were from Gallery, Library, Archive and Museum (GLAM)  institutions and a few educational and charitable ones too. Over the three days, here is what we covered:

Day one: Getting started

We kicked off with some introductory activities including “head, heart & hands” from Waldorf education. We looked at needs analysis (Who are the participants? What is the purpose?), adult learning (particularly Howard Gardners theory of multiple intelligences) and design skills (using David Kolb’s experiential learning and Bernice McCarthy’s 4MAT).

Day two: From theory to practice

The second day revisited design skills while touching on delivery skills and group work. This covered elocution, voice projection, body language and an examination the range of experiential activities that can be utilised in workshops. We also discussed aspects of Dave Meier’s accelerated learning (with feedback) and finished the day up with teams preparing for activities for day three.

Day three: The Show Must Go On

The final day of the course finished with the participants divided into four small teams. Each presented a on hour mini-session and had it critiqued by peers. This enabled us to learn from;

  • Our own mistakes
  • Other peoples mistakes
  • Copying / stealing other peoples good ideas, of which there were plenty. Thanks Abd, Daria, Doug, Eoin, Ian, Tara, Ian, Madeleine, Marianne, Saeeda, Tore, Sara and Bhav!

Overall, this was a really useful and memorable training course, one of the best training courses I’ve been on. The content, participants, location were all great and I felt empowered by taking the course as well as making useful contacts from a range of different organisations. It had a clearly defined purpose, well chosen activities and participants, with nothing irrelevant presented. There were tonnes of practical ideas to put into practice straight away which I look forward to doing in 2020. If you’d like to do the course, get in touch with Wikimedia UK.

While in Glasgow, it would be rude not to take advantage of all the bonus extra curricular activities the city has to offer:

Bonus 1: People Make Glasgow Hospitable 🏴󠁧󠁢󠁳󠁣󠁴󠁿

They say that People Make Glasgow, and Glaswegians are very hospitable. In between training sessions our host Sara showed us around the city, including the University cloisters (etc), Inn Deep on the banks of the River Kelvin and Curlers Rest in the West End. Sara’s impressive knowledge of Glasgow and its history is wikipedian in its depth and breadth.

Bonus 2: Glaswegian-Mancunian connections 🇬🇧

To me, Glasgow and Manchester feel like sibling cities separated at birth. If you’re English, Glasgow can feel like a Scottish Manchester. Perhaps Manchester feels like an English Glasgow to the Scots? Here is the case:

  • Second city syndrome 🥈: As second cities, both Glasgow and Manchester live in the shadow of their more famous capitals, Edinburgh and London. Both cities are the “belly and guts” of their respective nations. Glasgow had its docks, Manchester had its cotton. While both trades are long gone, they leave similar post-industrial legacies on the culture and infrastructure of their respective cities.
  • Shipping 🚢: Ships, shipping, docks, ports, quays and wharfs run deep in both cities. Glasgow built ships on the River Clyde while Manchester used ships for export and import of goods on its Ship Canal.
  • Football ⚽: Love it or loathe it, the fitbaw connection between Glasgow and Manchester is strong [1,2]. Scrolling through the list of Manchester United managers I count not just one, two or even three but FOUR Glaswegians. Matt Busby (Belshill is basically Glasgow), Tommy Docherty, Alex Ferguson and David Moyes. Is this a coincidence or catholicism? [1,2] Who knows, but my hypothesis is that being shouted at in a strong Glaswegian accent can make teams perform better (although it didn’t work very well for Moyes). I wonder how many Glasgow kisses Alex Ferguson gave his overpaid prima donna squad to keep them in line? Strangely, the fitbaw manager connection isn’t reciprocated: I can’t find any Mancunians in the list of Celtic managers or the list of Rangers managers

Bonus 3: King Tut’s Wah Wah Hut 🎸

Glasgow is home to the legendary King Tut’s Wah Wah Hut. This humble venue, relatively small with a capacity of only 300, has hosted an impressive range artists including Coldplay, Radiohead, Oasis, Blur, Pulp, Manic Street Preachers, you name it, they’ve played King Tut’s. Curious to find out what all the fuss was about, I arranged to meetup with an old Glaswegian friend for a drink at the venue. Assuming the gig that night would be sold out we asked at the bar who was playing. Turns out they had a handful of tickets left, so we spontaneously bought a pair to see Blanco White. Mixing Andalusian and Latin American influences, Blanco White play melancholic but beautiful tunes using a variety of instruments including the Charango [3]. Part of the reason King Tut’s is legendary is Glaswegian audiences are lively, and it was fun to see the band visibly moved by what Josh Edwards, the lead singer told us was:  “easily the best reception we’ve had in months of touring”.

Bonus 4: Like a Brudge over troubled water 🌊

Looking for a walk, run or ride in Glasgow? There are some great routes around the city like the Glasgow River Clyde Bridges, with at least 21 bridges to cross the Clyde on. On an early morning run, I couldn’t find any of the “bridges”, but there were plenty of “brudges” and some fantastic scenery along the Clyde. Och aye!

Bonus 5: The Glasgow Bucket List ☑️

There is still loads on my personal Glasgow bucket list for future visits, like the Kelvingrove Art Gallery and Museum, St. Mungo’s Cathedral, the Riverside Museum, Glasgow City Chambers, People’s Palace and the Glasgow Science Centre. What a great place Glasgow is, if you’ve never been, what are you waiting for?

References

    1. Frank Worrall (2007) Celtic United: Glasgow and Manchester – Two Football Clubs, One Passion, Mainstream publishing ISBN: 9781845962760
    2. Kieran Cunningham (2016) Alex Ferguson: The Irish Connection The Irish Daily Star, buzz.ie ☘️
    3. Blanco White: Olalla, more than a name…

July 17, 2019

Educating Computer Scientists: What should we discuss at #SIGCSE journal club?

fightclub

The first rule of journal club is, you do not talk about journal club. The second rule of journal club is, YOU DO NOT TALK ABOUT JOURNAL CLUB.* Discussions will go on as long as they have to. If this is your first time at journal club, you have to debate. Dress code: silly frocks and ridiculous hats are optional. Picture of my colleagues in the School of Computer Science ready for a graduation ceremony 2013, by Toby Howard.

So we’re starting a new Journal Club and Special Interest Group (SIG) for lecturers, teachers and course leaders in Manchester to discuss Computer Science Education (CSE). We’ll pick interesting papers, read them and then meet regularly to discuss them. It’s a bit like Fight Club but instead of beating each other up, we’ll “beat up” (review & critique) papers. Hopefully we’ll all learn something along the way. The first question to answer is, which papers should we discuss?

Computer Science (CS) is a young and professionally immature subject, it has only been taught at undergraduate level since 1965 in the UK. Across the pond in America, the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (sigcse.org) only started as recently as 1968, making it a very spritely fifty years young. On educational timescales, computer science is a whipper snapper! Fifty years is peanuts when you compare it to the millennia that mathematics has been taught for. In ancient Greece the earliest lessons were mathematics hence μάθημα (mathematics) means the lesson and derivatives like μαθαίνω (matheno) mean to learn or to know. While the greeks built some impressive analogue computers, digital computers and computer science as we now know it, did not exist in Ancient Greece. 🇬🇷 

What this means is that there is plenty of evidence about what works (and what doesn’t) when teaching mathematics. In contrast, how to teach Computer Science, what should be taught and why, to whom and when are all open questions

So, to get the ball rolling here are nine papers that tackle some of these open questions in Computer Science Education. We’ll vote on the three most interesting papers and read them before meeting to review them. Many of these papers are likely to be of interest to “educators” in its broadest sense. That means anyone teaching coding, computer science, tinkering, hacking and software/hardware engineering at any level. Which includes primary schools, code clubs, bootcamps, CoderDojos, hackathons, secondary schools, CPD programmes, K-12 education, lifelong learning, staff training courses, onboarding, induction, adult education programmes, return to work schemes and so on. If you’d like to join us we’ll be meeting in the Kilburn building, Manchester, M13 9PL (mosty likely first week of September, date and time tbc, drop me a line). Otherwise enjoy reading the insights below (DOI’s link to originals which may be behind a paywall, freely accessible versions are provided where available). Some papers are quite short, and have been selected for the topic they discuss rather than the quality of the content.

Twenty dirty tricks to train software engineers by Ray Dawson

A classic paper from Ray Dawson in the department of Computer Science at Loughborough University describing dirty tricks they use to introducing the frustrating realities of a software engineering development to students.

“Many employers find that graduates and sandwich students come to them poorly prepared for the every day problems encountered at the workplace. Although many university students undertake team projects at their institutions, an education environment has limitations that prevent the participants experiencing the full range of problems encountered in the real world. To overcome this, action was taken on courses at the Plessey Telecommunications company and Loughborough University to disrupt the students’ software development progress. These actions appear mean and vindictive, and are labeled ‘dirty tricks’ in this paper, but their value has been appreciated by both the students and their employers. The experiences and learning provided by twenty ‘dirty tricks’ are described and their contribution towards teaching essential workplace skills is identified. The feedback from both students and employers has been mostly informal but the universally favourable comments received give strong indications that the courses achieved their aim of preparing the students for the workplace. The paper identifies some limitations on the number and types of ‘dirty tricks’ that can be employed at a university and concludes that companies would benefit if such dirty tricks were employed in company graduate induction programmes as well as in university courses.”

Identifying student misconceptions of programming by Lisa Kaczmarczyk et al

This paper by Lisa Kaczmarczyk et al (formerly University of California, San Diego) recently came top of the ACM SIGCSE Top Ten Symposium Papers of All Time. In Lisa’s own words from the reinventing nerds podcast “The paper is sharing the results of a research study about misconceptions that novice computer science students have. Computer science is also a very abstract topic and the mistakes that students make are often baffling. The paper reports on the misconceptions that students have and why they have them. It’s important because this paper was the first to apply rigorous research methods to investigating misconceptions.” From the abstract:

“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.”

Stride in BlueJ – Computing for All in an Educational IDE by Michael Kölling et al

This paper by Michael Kölling et al describes an Integrated Development Environment (IDE) that combines the best features of visual programming languages (blockly, scratch etc) with text-based programming (such as Python, Java, C etc) for use in BlueJ.org.

“In introductory programming teaching, block-based editors have become very popular because they offer a number of strong advantages for beginning programmers: They avoid many syntax errors, can display all available instructions for visual selection and encourage experimentation with little requirement for recall. Among proficient programmers, however, text-based systems are strongly
preferred due to several usability and productivity advantages for expert users. In this paper, we provide a comprehensive introduction to a novel editing paradigm, frame-based editing – including design, implementation, experimentation and analysis. We describe how the design of this paradigm combines many advantages of block-based and text-based systems, then we present and discuss an implementation of such a system for a new Java-like language called Stride, including the results of several evaluation studies. The resulting editing system has clear advantages for both novices and expert programmers: It improves program representation and error avoidance for beginners and can speed up program manipulation for experts. Stride can also serve as an ideal stepping stone from
block-based to text-based languages in an educational context.”

  • Kölling, Michael; Brown, Neil C. C.; Hamza, Hamza; McCall, Davin (2019). “Stride in BlueJ — Computing for All in an Educational IDE”: Proceeding SIGCSE ’19 Proceedings of the 50th ACM Technical Symposium on Computer Science Education 63–69. DOI:10.1145/3287324.3287462

Ten quick tips for teaching programming by Neil Brown and Greg Wilson

This short paper from Neil Brown at King’s College London and Greg Wilson of software carpentry fame, is part of the popular Public Library of Science (PLOS) Ten Simple Rules series. The tips capture some ongoing research in listicle format.

“Research from educational psychology suggests that teaching and learning are subject-specific activities: learning programming has a different set of challenges and techniques than learning physics or learning to read and write. Computing is a younger discipline than mathematics, physics, or biology, and while there have been correspondingly fewer studies of how best to teach it, there is a growing body of evidence about what works and what doesn’t. This paper presents 10 quick tips that should be the foundation of any teaching of programming, whether formal or informal.

These tips will be useful to anyone teaching programming at any level and to any audience.”

How to Involve Students in FOSS Projects by Heidi Ellis et al

Initiatives like Google Summer of Code (GSoC) and Git going in FOSS aim to get students involved in Free and Open Source Software (FOSS) projects, through paid work and online tutorials. Some courses use FOSS projects to teach software engineering, though these are fairly unusual. How can we get more students (and teachers) involved in FOSS projects? This paper by Heidi J. C. Ellis provides some guidance

“Software projects are frequently used to provide software engineering students with an understanding of the complexities of real-world software development. Free and Open Source Software (FOSS) projects provide a unique opportunity for student learning as projects are open and accessible and students are able to interact with an established professional community. However, many faculty members have little or no experience participating in an open source software project. In addition, faculty members may be reluctant to approach student learning within such a project due to concerns over time requirements, learning curve, the unpredictability of working with a “live” community, and more. This paper provides guidance to instructors desiring to involve students in open source projects.”

  • Ellis, Heidi J. C.; Hislop, Gregory W.; Chua, Mel; Dziallas, Sebastian (2011). “How to involve students in FOSS projects” Frontiers in Education Conference (FIE) DOI:10.1109/FIE.2011.6142994 (ironically, if there is an open access version of this paper, I can’t find it! Another nominee for the Open Access Irony Awards)

A methodology for using GitLab for software engineering learning analytics by Julio César Cortés Ríos et al

This paper by Julio César Cortés Ríos at the University of Manchester describes using GitLab to analyse and improve courses.

“To bridge the digital skills gap, we need to train more people in Software Engineering techniques. This paper reports on a project exploring the way students solve tasks using collaborative development platforms and version control systems, such as GitLab, to find patterns and evaluation metrics that can be used to improve the course content and reflect on the most common issues the students are facing. In this paper, we explore Learning Analytics approaches that can be used with GitLab and similar tools, and discuss the challenges raised when applying those approaches in Software Engineering Education, with the objective of building a pipeline that supports the full Learning Analytics cycle, from data extraction to data analysis. We focus in particular on the data anonymisation step of the proposed pipeline to explore the available alternatives to satisfy the data protection requirements when handling personal information in academic environments for research purposes.”

Scaling Introductory Courses Using Undergraduate Teaching Assistants

Teaching computer science to large classes requires typically requires armies of teaching assistants, demonstrators. Your TA’s need to know their stuff and should be able to deal with students in a fair and consistent way. This paper is a medley of opinions from Jeffrey Forbes at Duke University, David Malan from Harvard University, Heather Pon-Barry from Mt. Holyoke College, Stuart Reges from the University of Washington and Mehran Sahami from Stanford University.

“Undergraduates are widely used in support of Computer Science (CS) departments’ teaching missions as teaching assistants, peer mentors, section leaders, course assistants, and tutors. Those undergraduates engaged in teaching have the opportunity to deeply engage with CS concepts and develop key communication and social competencies. As enrollments surge, undergraduate teaching assistants (UTAs) play a larger role in student experience and outcomes. While faculty and graduate student instructional support does not necessarily increase with the number of students in our courses, the number of qualified undergraduate teaching assistants for introductory CS courses should scale with the number of students in our courses. With large courses, the significance of the UTAs’ role in students’ learning likely also increases. Students have relatively little interaction with the instructor, and faculty may have more challenges monitoring and supporting individual UTAs. UTAs have a major role in affecting climate in computer science courses. The climate in large courses has substantial implications for students from groups traditionally underrepresented in computing. This panel will discuss how undergraduate teaching assistants can serve as a scalable effective teaching resource that benefits both the students in the course and the UTAs themselves.”

What Are We Doing When We Teach Computing & Programming by Sally Fincher

Two related papers by Sally Fincher at the University of Kent, the first published in 1999…

“The academic discipline of computer science uniquely prepares students for future study by teaching the fundamental construct of its practice-programming- before anything else. The disciplinary argument seems to run that if a student is not versed in the practicalities, then they cannot appreciate the underlying concepts of the discipline. This may be true. However an analogous simulation would be if it were thought necessary for architecture students to be taught bricklaying before they could appreciate the fundamentals of building design. This argument is clearly flawed when compared to endeavours such as the study of English Literature, which makes no claim to teach the practice of producing work before the study of the products of others work. It is possible that this is an argument of disciplinary maturity-that all disciplines have passed through a similar phase. This paper examines the emergent approaches being defined, all of which address the central concern of the teaching of programming and its relationship to the learning of computer science. It examines: the “syntax-free” approach of Richard Bornat and Russel Shackelford, the “problem-solving” approach of David Barnes (et al.), the “literacy” approach of Peter Juliff and Owen Astrachan and the “computation-as-interaction” approach of Lynn Andrea Stein. These approaches are discussed both in their own terms, and also placed in a preliminary taxonomic framework for the teaching of programming.”

….and the second published in 2015 (see comments on Mark Guzdial’s summary):

“Research on the cognitive, educational, and policy dimensions of teaching computing is critical to achieving “computer literacy.”

Making CS Learning Visible: Case Studies on How Visibility of Student Work Supports a Community of Learners in CS Classrooms by Amber Solomon et al

This is a paper by Amber Solomon et al from the Innovation and Technology in Computer Science Education (ITiCSE) conference is about reducing defensive and competitive (macho?) cultures in Computer Science  (via Mark Guzdials blog).

Modern learning theories emphasize the critical social aspect of learning. Computer science (CS) classrooms often have “defensive climates” that inhibit social learning and prevent the development of a community of learners. We believe that we can improve the social context of computer science learning by expanding CS learning beyond the single student in front of a display screen. Our theory is that the single student and single display inhibits collaboration and collaborative awareness of student work. In this paper, we present two case studies where we explored ways to make student work visible to peers. The first case study involved using a studio model for learning enabled by projection-based Augmented Reality (AR), and the second case study involves using a maker-oriented curriculum to make student work visible. Findings suggest the visibility of student work in CS classrooms helped support a community of learners: students collaborated, used each other as sources of inspiration, and felt more comfortable asking for help.

References and notes

*”You do not talk about Journal Club” is an adapted quote from the 1999 film Fight Club, see below. I’m only joking, you are of course welcome to talk to anyone who will listen about Journal Club.

Talking of David Malan, you can see his talk on making CS50 scale when he visited Manchester in 2017

June 23, 2017

Nine ideas for teaching Computing at School from the 2017 CAS conference

CAS

Delegates at the Computing at School conference 2017 #CASConf17 answering diagnostic questions, picture by Miles Berry.

The Computing At School (CAS) conference is an annual event for educators, mostly primary and secondary school teachers from the public and private sector in the UK. Now in its ninth year, it attracts over 300 delegates from across the UK and beyond to the University of Birmingham, see the brochure for details. One of the purposes of the conference is to give teachers new ideas to use in their classrooms to teach Computer Science and Computational Thinking. I went along for my first time (*blushes*) seeking ideas to use in an after school Code Club (ages 7-10) I’ve been running for a few years and also for approaches that undergraduate students in Computer Science (age 20+) at the University of Manchester could use in their final year Computer Science Education projects. So here are nine ideas (in random brain dump order) I’ll be putting to immediate use in clubs, classrooms, labs and lecture theatres:

  1. Linda Liukas demonstrated some intriguing ideas from her children’s books and HelloRuby.com that are based on Montessori education. I shall be trying some of these out (particularly the storytelling stuff) at code club to keep girls involved
  2. Sue Sentance and Neil Brown from King’s College London gave an overview of some current research in pedagogy.  They discussed research questions that can be tackled in the classroom like (for example) do learners make more progress using visual programming languages (like Scratch and Blockly) or traditional text-based languages (like Python and Java etc)? Many of these research questions would make good projects for undergraduate students to investigate in secondary schools, see research on frame based editors, for example.
  3. Michel Wermelinger from the Open University demonstrated using iPython notebooks for teaching data literacy at the Urban Data School. Although I’m familiar with iPython, it had never occurred to me to actually use iPython in school for teaching. It is a no-brainer, when you think about it, even for primary, because you have your code, inputs and outputs all in one window, and can step through code execution instead of (or as well as) using more conventional tools like Trinket, Thonny or IDLE. Data literacy is fun to teach.
  4. Miles Berry from the University of Roehampton demonstrated Diagnostic Questions in Project Quantum. These are a collection of high quality quizzes to use interactively for example as hinge questions, where teaching is adapted depending on answers given, like this multiple choice question:
    Consider the following Python code:
    
    a = 20
    b = 10
    a = b
    
    What are the values of a and b?
    
    A: a = 10, b = 10
    B: a = 20, b = 20
    C: a = 30, b = 10
    D: a = 10, b = 20
    

    You’ll have to try these five questions to check your answer. The useful thing here is that DiagnosticQuestions.com (the platform on which this is built) allows you to see lots of responses, for example each answer (A, B, C or D) above was selected by 25% of participants. You can also view explanations which illuminate common misconceptions (e.g. the classic mistake of confusing assignment with equality) as well as providing a bank of free questions for use in the classroom.

  5. Mark Guzdial from GeorgiaTech discussed using learning sciences to improve computing teaching. He demonstrated predictive questions (e.g. ask students What do you think will happen when we run this code? before actually executing it) alongside what he called subgoal labelling. These are simple ideas (with proven benefits) that can be put to use immediately. I’ll also be trying the Live Coding (with Sonic Pi) and Media Computation he demonstrated asap.
  6. Laurence Rogers demonstrated Insight: Mr. Bit  this looks like a good app for using BBC microbits in the classroom, connected to a range of sensors, provided you’ve got access to iPads.
  7. A copy of Hello World magazine was in the conference bag. The summer 2017 issue has an unusual article from Ian Benson from Kingston University and Jenny Cane describing their use of the Haskell programming language to teach 5-7 year olds to reason symbolically and learn algebra before arithmetic with help from Cuisenaire rods. The Scratch Maths project at University College London are doing similar things, building mathematical knowledge using Scratch, rather than Haskell. These are experimental ideas you could try out on unsuspecting (junior) family members.
  8. Lee Goss from Barefoot Computing, described the free CPD for primary school teachers on offer from BT. I’ve signed up and hope to plug some of the shortcomings in the Code Club Curriculum.
  9. Richard Jarvis demonstrated appJar, a handy Python library for teaching Graphical User Interfaces (GUIs). That’s Jar as in Jarvis and Jam, not JAR as in Java ARchive BTW. I’ve not tried GUIs at code club yet, but appJar looks like a good way to do it.

There were lots more people and projects at the conference not mentioned here including tonnes of workshops. If you’re interested in any of the above, the CAS conference will be back in 2018. Despite the challenging problems faced by Computer Science at GCSE level, it was reassuring and inspiring to meet some members of the vibrant, diverse and friendly community pushing the boundaries of computing in schools across the United Kingdom. Thanks again to everyone at CAS for putting on another great event, I will definitely consider attending next year and maybe you should too.

Next Page »

Blog at WordPress.com.