Research Strands

The Centre for Computing Science Education supports several ongoing strands of research. Many of these strands have ongoing projects assocaited with them, which can be read about below.

Mental Models in Programming

Peter Donaldson is currently investigating conceptual models of programming languages and systems (notional machine models) and which types of activities help novices to develop a more coherent and accurate mental model of how they work. Development of a coherent mental model of program execution is increasingly seen by the CS Education community as an important developmental step in learning to program and therefore a key aspect in educators being able to support all learners to become computationally literate. Peter’s research draws inspiration from prior work on mental models and current research in scientific modelling and coherence formation using multiple external representations.


Early Play and Programming

Elizabeth Cole is a PhD student investigating the role of early play in the development of foundational programming skills. Her initial research has triangulated theories about play-based skills development in children under the age of eight by exploring three related categories: the importance of computing science skills in non-digital careers, the connection between adults' programming ability and their childhood play habits and the success of children undertaking block programming in formal education. The emerging results of the latter indicate that low-tech play experiences matter for the pupils, whereas socio-economic status doesn't.

The future of this project will involve the analysis of two concepts: comprehension of the structural appearance of code and how it operates during execution. Both of these concepts corelate well with children’s emergent literacy skills.


Graphical Representations of Statistical Cognition

Lovisa Sundin completed her BSc degree in computing science and psychology at University of Glasgow in 2017, and is now a second-year PhD student. Across STEM, many graduates are now expected to not only have a grasp of common statistical procedures, but also skills in statistical programming. Her research is concerned with both, by evaluating ways of helping novices implement statistical procedures programmatically. Specifically, she is interested in a range of graphical techniques meant to address various cognitive aspects of this process: the conceptual, the computational and the syntactic.

There is currently an active project associated with this research: Slice N Dice.


Work Based Learning

The Department of Computer Science at the University of Glasgow will soon be launching its very first Graduate Apprenticeship in Software Engineering. Graduate Apprenticeships are work-based degrees, awarding a qualification equal to the standard academic route through Computer Science with substantial industry experience to boot.

The degree has been developed from scratch, drawing in research from international best practice concerning work-based learning, communities of practice, active learning and more. The work has been supported by international visits, large scale industry consultation and extensive literature review across several areas, all contributing to the development of a programme which we believe to provide an excellent learning experience for students and a valuable investment for employers.

More information about the development of this programme can be found here under Work Based Learning, and information on the programme itself can be found on the School site under .


Programming Language Transfer for Novices

Ethel Tshukudu is a PhD student in Computing Science at the University of Glasgow investigating conceptual transfer for relative novices when they transition from one programming language to another as they progress in their level of Computing Science Education. This area of research is relevant to the Computing Science school education context because students find themselves faced with the need to transfer from blocks to text programming languages or procedural languages to object-oriented languages. Ethel intends on developing a framework of conceptual transfer for relative novices as well as developing a pedagogy that is suitable for conceptual transfer.


Spatial Skills

Jack Parkinson and Quintin Cutts have been researching the connection between spatial ability and success in Computing Science at a university level. It has been discovered that, along with many STEM subjects, success in computing courses at university correlate with one’s spatial skills: that is, their ability to mentally manipulate 2D and 3D objects, identify visual patterns and other spatial cognitive tasks.

Jack’s MSci project in 2017 strengthened our understanding of this correlation by showing that people further advanced in their computing careers had stronger spatial skills on average than those starting out, and suggested a theoretical model for the relationship between spatial ability and success in Computing Science. This work was published and was well received by the Computing Science Education community, being awarded the Chair’s Award for best paper at the International Computing Science Education conference 2018.

Jack began his PhD in this area in 2019.


CCSE in Schools

  • PhD students will shortly be working with groups of teachers on collaborative projects, involving the development of effective pedagogy for teaching CS at primary school level, and on the importance of spatial skills to learning in CS at primary and secondary levels.
  • The CCSE is a lead architect of the Scottish school curriculum for computing science, for 3-15 year olds.  This curriculum draws on experience of working with pupils and teachers, on leading curricula from around the world, and from a deep analysis of the broad skills and understanding required of a computer scientist or engineer.  Two guides to the curriculum, one specifically for primary teachers and the other aimed at Secondary teachers, can be found at http://teachcs.scot.
  • Professor Cutts sits on the academic board of the National Centre for Computing Education, to provide advice on its development and operation.  This is a UK Government-funded £84M initiative to learning resources and professional development for teachers.  He is a member of the UK Government’s digital skills working group and has an adjunct position with the University of Oslo, working with colleagues to enhance computing education in Norwegian schools.
  • Mr Donaldson is the Higher Education representative on the Scottish Qualifications Authority National Qualifications Support Team for Computing Science and Sector panel for Computing.  He is also a member of the CAMAU project research team for the Science and Technology Area of Learning and Experience, providing policy and research based guidance on learning progression to Welsh Pioneers shaping the new Curriculum for Wales. Mr Donaldson and Dr Singer are currently leading a CCSE project funded by the DataLab to create a FutureLearn MOOC that introduces basic Data Science to educators of all backgrounds.
  • Previously, Professor Cutts and Mr Donaldson led the Professional Learning and Networking in Computing project, funded by the Scottish Government, to provide professional learning for Scottish computing teachers.  A network of 25 teacher hubs was set up around the country and a sequence of professional development activities created, particularly exploring a range of novel research-led CS pedagogy. Most of the local hub support materials and example exercises can be found in the CPD Resources for pedagogical content knowledge (PLANC) resource on the Computing At School Community site.
  • CCSE staff advised on the new national school Computing Science qualifications, in particular recommending the importance of program comprehension.  This led to further work on how to fairly assess program comprehension, and the development of a reference language, jovially referred to as Haggis, for use in national examinations in Scotland. An online implementation of National 5 and Higher versions of the language can be found at https://haggis4sqa.appspot.com/haggisParser.html.