Predictive Modelling for HCI Problems in Novice Program Editors

McKay, Fraser and Kölling, Michael (2013) Predictive Modelling for HCI Problems in Novice Program Editors. In: HCI 2013 - The 27th International British Computer Society Human Computer Interaction Conference: The Internet of things, 9-13 September 2013, London, UK. (Full text available)

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Abstract

We extend previous cognitive modelling work to four new programming systems, with results contributing to the development of a new novice programming editor. Results of a previous paper, which quantified differences in certain visual languages, and feedback we had regarding interest in the work, suggested that there may be more systems to which the technique could be applied. This short paper reports on a second series of models, discusses their strengths and weaknesses, and draws comparisons to the first. This matters because we believe “bottlenecks” in interaction design to be an issue in some beginner languages – painfully slow interactions may not always be noticeable at first, but start to become intrusive as the programs grow larger. Conversely, text-based languages are generally less viscous, but often use difficult symbols and terminology, and can be highly error-prone. Based on the models presented here, we propose some simple design choices that appear to make a useful and substantive difference to the editing problems discussed.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Cognitive models, programming, Scratch, Alice, StarLogo, CogTool, cognitive dimensions, viscosity
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computing Education Group
Depositing User: F. Mckay
Date Deposited: 12 Sep 2013 17:10 UTC
Last Modified: 12 Jan 2017 14:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34950 (The current URI for this page, for reference purposes)
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