|Room||Owheo Building, Room 2.53|
|Phone||+64 3 479 8314|
My background is in psychology and linguistics at the University of Canterbury (NZ), and cognitive science at the University of Sussex (UK). I joined the department as a lecturer in artificial intelligence in August 1989. My main areas of research are neural networks and computer science education.
I am interested in neural networks as a tool for modeling cognition, with a particular focus on modeling aspects of memory and forgetting. Much of my research has explored the problem known as "catastrophic forgetting", and whether the "pseudorehearsal" solution that I propose has anything to do with dreams (the consolidation of learning during sleep). I am part of the artificial intelligence group, which includes such topics such as defeasible reasoning.
My second main research focus is computer science education, particularly the teaching and learning of a first programming language. Introductory "CS1" programming courses typically have very rates of both failing and of excellent grades (with fewer "mid-range" grades than usual). I think that we can make sense of this apparent paradox in terms of the mechanisms of learning and the unusually dense / interconnected nature of programming language constructs. I have been involved in two international studies of novice programmers, the Scaffolding and BRACE projects.
I teach and coordinate COMP160 the Department's introductory programming paper (Java), and COSC420 Neural Networks. In 2012 I was awarded an Ako Aotearoa Tertiary Teaching Excellence Award. I love teaching at all levels, and have been involved with many robotics projects for local high schools, including helping to run the annual RoboCup Junior Otago competition.
Robins, A. Learning Edge Momentum. In Norbert Seel (Ed) Encyclopedia of the Sciences of Learning. Berlin: Springer. Volume 4, 1845 - 1848 (2011)
Robins A. Learning edge momentum: A new account of outcomes in CS1. Computer Science Education, 20, 37 - 71 (2010)
Abraham, C. & Robins A. Memory retention - the synaptic stability versus plasticity dilemma. Trends in Neuroscience, 28(2), 73 - 78 (2005).
Robins, A. & McCallum, S. A robust method for distinguishing between learned and spurious attractors. Neural Networks, 17, 313 - 326 (2004).
Robins, A., Rountree, J. & Rountree, N. Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137 - 172 (2003).
For a full list of publications, please check my Google Scholar profile.