O'Really?

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.”

• Dawson, Ray (2000). Twenty dirty tricks to train software engineers. Proceedings of the 22nd International Conference on Software Engineering (ICSE ’00 ) Pages 209-212, Limerick, Ireland — June 04 – 11, 2000 DOI:10.1145/337180.337204

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.”

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

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.”

• Brown Neil C.C., Wilson, Greg (2018) Ten quick tips for teaching programming. PLoS Computational Biology 14(4): e1006023. DOI:10.1371/journal.pcbi.1006023

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.”

• Rios, JCC et al (2019) A Methodology for Using GitLab for Software Engineering Learning Analytics Proceeding CHASE ’19 Proceedings of the 12th International Workshop on Cooperative and Human Aspects of Software Engineering dl.acm.org/citation.cfm?id=3338720 broken DOI: 10.1109/CHASE.2019.00009

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.”

• Forbes, Jeffrey; Malan, David J.; Pon-Barry, Heather; Reges, Stuart; Sahami, Mehran (2017). Scaling Introductory Courses Using Undergraduate Teaching Assistants: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education Pages 657-658. DOI:10.1145/3017680.3017694

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.”

• Fincher, Sally (1999) What are we doing when we teach programming?. 1: 12 1–15. DOI:10.1109/FIE.1999.839268
• Fincher, Sally (2015) What are we doing when we teach computing in schools?. Communications of the ACM. 58 (5): 24–26. DOI:10.1145/2742693

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.

• Solomon, A et al (2019) Making CS Learning Visible: Case Studies on How Visibility of Student Work Supports a Community of Learners in CS Classrooms, ITiCSE ’19 Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education Pages 161-167 Aberdeen, Scotland UK. DOI:10.1145/3304221.3319791

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