O'Really?

September 6, 2021

Join us to discuss why computing students should contribute to open source software projects on Mon 6th September at 2pm BST

unlocked padlock by flaticon.com

Why should students bother with open source software? Join us to discuss why via a viewpoint piece published by Diomidis Spinellis of Athens University and Delft University of Technology published in the July issue of Communications of the Association for Computing Machinery. [1] Here’s the introduction 

Learning to program is—for many practical, historical, as well as some vacuous reasons—a rite of passage in probably all computer science, informatics, software engineering, and computer engineering courses. For many decades, this skill would reliably set computing graduates apart from their peers in other disciplines. In this Viewpoint, I argue that in the 21st century programming proficiency on its own is neither representative of the skills that the marketplace requires from computing graduates, nor does it offer the strong vocational qualifications it once did. Accordingly, I propose that computing students should be encouraged to contribute code to open source software projects through their curricular activities. I have been practicing and honing this approach for more than 15 years in a software engineering course where open source contributions are an assessed compulsory requirement. Based on this experience, I explain why the ability to make such contributions is the modern generalization of coding skills acquisition, outline what students can learn from such activities, describe how an open source contribution exercise is embedded in the course, and conclude with practices that have underpinned the assignment’s success

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

References

  1. Spinellis, Diomidis (2021). “Why computing students should contribute to open source software projects”. Communications of the ACM64 (7): 36–38. DOI:10.1145/3437254

July 7, 2021

Would YOU want to live in Alan Turing’s house?

The blue plaque on Alan Turing’s house, commemorating his work in cryptography which founded both Computer Science and Artificial Intelligence as new disciplines. Picture by Joseph Birr-Pixton on Wikimedia Commons w.wiki/3aYW

The house where Computer Scientist Alan Turing spent his final years is currently up for sale. The estate agent describes the property on 43 Adlington Road, Wilmslow as a Victorian family residence of significant historical importance. Wilmslow and the surrounding Cheshire countryside is popular with Manchester commuters, including many Man United, Man City & England football stars. Even if you could afford its premier league price tag, would YOU want to live in the house where Turing’s life ended so tragically? 

Turing was found dead at this house, on the 8th June 1954 by his cleaner. The cause of his death the previous day was established as cyanide poisoning. He was just 41 years old. When his body was discovered, an apple lay half-eaten by his bedside. 

The coroner recorded a verdict of suicide.

At the end of his life Turing was suffering mentally and physically. The homophobic British authorities were using a form of legalised torture, known as forced chemical castration, to punish him for being homosexual. At the time, homosexuality was a crime. Turing put on a brave face and joked about his castration (“I’m growing breasts!), but it must have been horrible to endure.

If you’re feeling suicidal or tortured, you don’t have to struggle with difficult feelings alone. If you’re suffering from emotional distress or struggling to cope a Samaritan can face your problems with you. Whatever you’re going through, samaritans.org are available 24 hours a day, 365 days a year. They respond to around 10,000 calls for help every day. No judgement. No pressure. Call them free any time, from any phone on 116 123.

While everyone can have a good old nosey at Turing’s house through the estate agents window, no-one needs to suffer like its famous former resident did. Personally I think I’d find this property an enigmatically haunted house to live in, knowing that this was the place where a great man’s life ended in such tragedy. How about you?

Turing’s House: Copper Folly, 43 Adlington Road, Wilmslow, Cheshire, SK9 2BJ

  1. Rightmove details www.rightmove.co.uk/properties/109329428
  2. Savills.com details in a single pdf file bit.ly/alan-turings-house
  3. Turing’s house in Google maps goo.gl/maps/krMM3A2JfgTUVFfm8
  4. GCSE computing: Alan Turing: Creator of modern computing bbc.co.uk/teach/alan-turing-creator-of-modern-computing/zhwp7nb
  5. Alan Turing’s Manchester by Jonathan Swinton describes what it was like to make new friends and lovers in the smog-bound, bombed-out city of Manchester from 1948 to 1954 manturing.net
  6. Leslie Ann Goldberg, Simon Schaffer and Andrew Hodges discuss Turing’s ideas and life with Melvyn Bragg https://www.bbc.co.uk/programmes/m000ncmw
  7. Breast enlargement in men undergoing chemical castration https://en.wikipedia.org/wiki/Gynecomastia

Acknowledgements

Thanks to Alan O’Donohoe for spotting Turing’s house on the market and to Joseph Birr-Pixton for publishing his picture of Turing’s blue plaque on Wikimedia Commons.

June 3, 2021

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

Filed under: education — Duncan Hull @ 8:07 am
Tags: , , , ,

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

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

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

References

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

May 5, 2021

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

Filed under: education — Duncan Hull @ 11:13 am
Tags: , , , , , ,
Thinking icon via flaticon.com

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

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

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

References

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

April 15, 2021

I wish I’d read this book when I was doing my PhD!

Anyone for a game of PhD bingo?

Published this year by Oxford University Press, How to Get Your PhD: A Handbook for the Journey by Gavin Brown [1] is essential reading for anyone thinking of doing, or trying to get through, a PhD. I wish I’d had this book when I was doing mine, here’s why:

I thoroughly enjoyed my PhD and given the chance I’d do it all again. I was lucky to be able to do research guided by a great supervisor (Robert Stevens) and it was rewarding being part of a big and friendly lab. There were loads of opportunities to get involved in all sorts of other projects along the way. Thankfully, I also had some good mentors and met tonnes of interesting people from all over the world. I am very grateful to Robert, Carole Goble and everyone else who made it possible.

Despite all the good stuff, there’s plenty I could have done better. Hindsight is a great teacher. Gavin’s book would have helped me do a better PhD but hadn’t been written at that time – I wish it had been. I wish that I knew what I know now, when I was younger. [2]

Alongside serious technical advice on the mechanics of doing a PhD, Gavin’s book provides a good overview of some the psychological and emotional hurdles every PhD will encounter. Unlike a lot of similar books (there’s already tonnes of self-help PhD guides out there), this one is written in first person singular which makes for a more engaging and shorter read. Serious advice is balanced by the books light hearted tone, with plenty of humour, such as the game of PhD Bingo, shown in the picture on the right. Like most students, I ticked all those boxes (BINGO!) apart from the “you will read this book” box. Don’t be that person, Read The Friendly Manual! RTFM. Read THIS Friendly Manual!

The handbook also includes personal stories which help get key messages across, not just from Gavin, but a distinguished bunch of scientists, engineers and mathematicians who have contributed to the second part of the book including Nancy RothwellVictoria BurnsSteve FurberLucy KissickHiranya PeirisMelanie LengJeremy WyattDavid HandCarolyn VircaShakir MohamedJonny Brooks-Bartlett and Jennifer Polk.

So if you’re wondering about doing a PhD, or you’re currently doing one, go and read Gavins book. I’m not just saying that because (disclaimer) Gavin is a colleague of mine. I’m saying that because I wish this book had existed back when I did my PhD. It’s packed full of sound advice and I heartily recommend you read it!

References

  1. Brown, Gavin (2021) How to Get Your PhD: A Handbook for the Journey, Oxford University Press, ISBN:9780198866923
  2. Lane, Ronnie and Wood, Ronnie (1973) “Ooh La La.” In Ooh La La. The Faces. “I wish that I knew what I know now, when I was younger…”

January 18, 2021

Join us to discuss failure rates in introductory programming courses on 1st Feb at 2pm GMT

Filed under: education — Duncan Hull @ 12:39 pm
Tags:
Icons made by freepik from flaticon.com

Following on from our discussion of ungrading, this month we’ll be discussing pass/fail rates in introductory programming courses. [1] Here is the abstract:

Vast numbers of publications in computing education begin with the premise that programming is hard to learn and hard to teach. Many papers note that failure rates in computing courses, and particularly in introductory programming courses, are higher than their institutions would like. Two distinct research projects in 2007 and 2014 concluded that average success rates in introductory programming courses world-wide were in the region of 67%, and a recent replication of the first project found an average pass rate of about 72%. The authors of those studies concluded that there was little evidence that failure rates in introductory programming were concerningly high.

However, there is no absolute scale by which pass or failure rates are measured, so whether a failure rate is concerningly high will depend on what that rate is compared against. As computing is typically considered to be a STEM subject, this paper considers how pass rates for introductory programming courses compare with those for other introductory STEM courses. A comparison of this sort could prove useful in demonstrating whether the pass rates are comparatively low, and if so, how widespread such findings are.

This paper is the report of an ITiCSE working group that gathered information on pass rates from several institutions to determine whether prior results can be confirmed, and conducted a detailed comparison of pass rates in introductory programming courses with pass rates in introductory courses in other STEM disciplines.

The group found that pass rates in introductory programming courses appear to average about 75%; that there is some evidence that they sit at the low end of the range of pass rates in introductory STEM courses; and that pass rates both in introductory programming and in other introductory STEM courses appear to have remained fairly stable over the past five years. All of these findings must be regarded with some caution, for reasons that are explained in the paper. Despite the lack of evidence that pass rates are substantially lower than in other STEM courses, there is still scope to improve the pass rates of introductory programming courses, and future research should continue to investigate ways of improving student learning in introductory programming courses.

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

Thanks to Brett Becker and Joseph Allen for this months #paper-suggestions via our slack channel at uk-acm-sigsce.slack.com.

References

  1. Simon, Andrew Luxton-Reilly, Vangel V. Ajanovski, Eric Fouh, Christabel Gonsalvez, Juho Leinonen, Jack Parkinson, Matthew Poole, Neena Thota (2019) Pass Rates in Introductory Programming and in other STEM Disciplines in ITiCSE-WGR ’19: Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education, Pages 53–71 DOI: 10.1145/3344429.3372502

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!

Next Page »

Blog at WordPress.com.