Predictors for success in studying CS

Nathan Rountree*, Tamar Vilner, Brenda Cantwell Wilson, Roger Boyle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)


University education in all disciplines is changing fast. A global 'massification' is causing intakes to swell, and students are becoming more focused on vocational issues. When universities were smaller and attitudes different, it was simple for departments to recruit students who were relatively certain to succeed this has changed, Nowadays universities cannot 'cherry pick' the high quality output of high schools, and motivation of the intake to study has evolved far away from the pure pursuit of knowledge for which we might once have hoped. CS is affected by this in several peculiar ways: (1) It is often popular among applicants for the 'wrong' reasons: a route to a job. This is true despite the 'dotcom' collapse. (2) In many countries, pre-university CS is not easily available. Where it is available, its content and delivery is unlikely to correspond well to the university curriculum. (3) Many students have some experience of computing, often on home machines and self-taught. This can give them a wholly misleading view of what CS is, and their aptitude for it. (4) The math content of CS is often unexpected by students, and provokes a negative reaction. (5) Pre-university math preparation, while clearly of value, is often not well suited to CS. While mathematical fluency and algebraic fluency are clearly useful, the maths of CS (logic, discrete maths) are often untouched by high schools. For these, and other reasons, it can be very hard to predict the performance of CS students at entry. Members of the panel have statistical evidence from a range of countries and university systems demonstrating this to be the case. The desirability of being able to predict who will succeed is clear: If high school grades are incomplete or inaccurate predictors, what should we use instead? Meanwhile, attempts to widen participation, which are global, mean that many applicants present without traditional qualifications, and we need mechanisms to judge them. Further, filtering out those who will fail will save resources and time of both staff and students, Little work has been addressed to these issues. The panel members have looked at various aspects of the problem independently, and bring different research and attitudes to it. The questions to which we address ourselves are What, in high school qualifications, might be a good indicator for study of CS at university? For which parts of the university curriculum is this an indicator? To what extent is math essential to successful university CS study? Which aspects of the math curriculum? Is high school math a good, or imperfect, indicator of CS success? The need for students to be able to program is acknowledged; what are the indicators for this? Is pre-university programming experience useful or misleading? What student expectations are important? If some expectation is important, how might it be measured prior to entry? How might we attract the 'right sort' of students into CS when they have not (yet) considered the option? What indicators might we use for non-traditional entrants, such as mature students with weak or outdated high school qualifications?

Original languageEnglish
Pages (from-to)145-146
Number of pages2
JournalSIGCSE Bulletin (Association for Computing Machinery, Special Interest Group on Computer Science Education)
Issue number1
Publication statusPublished - Mar 2004
Externally publishedYes
Event35th SIGCSE Technical Symposium on Computer Science Education - Norfolk, VA, United States of America
Duration: 03 Mar 200407 Mar 2004


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