Anthony V. Robins

Learning and Teaching Programming: A Review and Discussion

In this paper we review the literature relating to the psychological/educational study of programming. We identify general trends comparing novice and expert programmers, programming knowledge and strategies, program generation and comprehension, and object-oriented versus procedural programming. (We do not cover research relating specifically to other programming styles.) The main focus of the review is on novice programming and topics relating to novice teaching and learning. Various problems experienced by novices are identified, including issues relating to basic program design, to algorithmic complexity in certain language features, to the “fragility” of novice knowledge, and so on. We summarise this material and suggest some practical implications for teachers. We suggest that a key issue that emerges is the distinction between effective and ineffective novices. What characterises effective novices? Is it possible to identify the specific deficits of ineffective novices and help them to become effective learners of programming?

Memory retention - the synaptic stability versus plasticity dilemma

Memory maintenance is widely believed to involve long-term retention of the synaptic weights that are set within relevant neural circuits during learning. However, despite recent exciting technical advances, it has not yet proved possible to confirm experimentally this intuitively appealing hypothesis. Artificial neural networks offer an alternative methodology as they permit continuous monitoring of individual connection weights during learning and retention. In such models, ongoing alterations in connection weights are required if a network is to retain previously stored material while learning new information. Thus, the duration of synaptic change does not necessarily define the persistence of a memory; rather, it is likely that a regulated balance of synaptic stability and synaptic plasticity is required for optimal memory retention in real neuronal circuits.

Learning Edge Momentum: A New Account of Outcomes in CS1

Compared to other subjects, the typical introductory programming (CS1) course has higher than usual rates of both failing and high grades, creating a characteristic bimodal grade distribution. In this article, I explore two possible explanations. The conventional explanation has been that learners naturally fall into populations of programmers and non-programmers. A review of decades of research, however, finds little or no evidence to support this account. I propose an alternative explanation, the learning edge momentum (LEM) effect. This hypothesis is introduced by way of a simulated model of grade distributions, and then grounded in the psychological and educational literature. LEM operates such that success in acquiring one concept makes learning other closely linked concepts easier (whereas failure makes it harder). This interaction between the way that people learn and the tightly integrated nature of the concepts comprising a programming language creates an inherent structural bias in CS1, which drives students towards extreme outcomes.

Interacting factors that predict success and failure in a CS1 course

The factors that contribute to success and failure in introductory programming courses continue to be a topic of lively debate, with recent conference panels and papers devoted to the subject (e.g. Rountree et al. 2004, Ventura et al., 2004, Gal-Ezer et al., 2003). Most work in this area has concentrated on the ability of single factors (e.g. gender, math background, etc.) to predict success, with the exception of Wilson et al. (2001), which used a general linear model to gauge the effect of combined factors. In Rountree et al. (2002) we presented the results of a survey of our introductory programming class that considered factors (such as student expectations of success, among other things) in isolation. In this paper, we reassess the data from that survey by using a decision tree classifier to identify combinations of factors that interact to predict success or failure more strongly than single, isolated factors.

Transfer in cognition

The purpose of this paper is to review the cognitive literature regarding transfer in order to provide a context for the consideration of transfer in neural networks. We consider transfer under the three general headings of analogy, skill transfer and metaphor. The emphasis of the research in each of these areas is quite different and the literatures are largely distinct. Important common themes emerge, however, relating to the role of similarity, the importance of “surface content”, and the nature of the representations that are used. We will draw out these common themes, and note ways of facilitating transfer. We also briefly note possible implications for the study of transfer in neural networks.

A Case Study of the Introduction of Computer Science in NZ Schools

For many years computing in New Zealand schools was focused on teaching students how to use computers, and there was little opportunity for students to learn about programming and computer science as formal subjects. In this article we review a series of initiatives that occurred from 2007 to 2009 that led to programming and computer science being made available formally as part of the National Certificate in Educational Achievement (NCEA), the main school-leaving assessment, in 2011. The changes were phased in from 2011 to 2013, and we review this process using the Darmstadt model, including describing the context of the school system, the socio-cultural factors in play before, during and after the changes, the nature of the new standards, the reactions and roles of the various stakeholders, and the teaching materials and methods that developed. The changes occurred very quickly, and we discuss the advantages and disadvantages of having such a rapid process. In all these changes, teachers have emerged as having a central role, as they have been key in instigating and implementing change.

Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

Recent studies have explored the organization of player movements in team sports using a range of statistical tools. However, the factors that best explain the performance of association football teams remain elusive. Arguably, this is due to the high-dimensional behavioural outputs that illustrate the complex, evolving configurations typical of team games. According to dynamical system analysts, movement patterns in team sports exhibit nonlinear self-organizing features. Nonlinear processing tools (i.e. Artificial Neural Networks; ANNs) are becoming increasingly popular to investigate the coordination of participants in sports competitions. ANNs are well suited to describing high-dimensional data sets with nonlinear attributes, however, limited information concerning the processes required to apply ANNs exists. This review investigates the relative value of various ANN learning approaches used in sports performance analysis of team sports focusing on potential applications for association football. Sixty-two research sources were summarized and reviewed from electronic literature search engines such as SPORTDiscus™, Google Scholar, IEEE Xplore, Scirus, ScienceDirect and Elsevier. Typical ANN learning algorithms can be adapted to perform pattern recognition and pattern classification. Particularly, dimensionality reduction by a Kohonen feature map (KFM) can compress chaotic high-dimensional datasets into low-dimensional relevant information. Such information would be useful for developing effective training drills that should enhance self-organizing coordination among players. We conclude that ANN-based qualitative analysis is a promising approach to understand the dynamical attributes of association football players.

Computer science in NZ high schools: the first year of the new standards

Computer science became available as a nationally assessed topic in NZ schools for the first time in 2011. We review the introduction of computer science as a formal topic, including the level of adoption, issues that have arisen in the process of introducing it, and work that has been undertaken to address those issues.

Local Learning Algorithms for Sequential Tasks in Neural Networks

In this paper we explore the concept of sequential learning and the efficacy of global and local neural network learning algorithms on a sequential learning task. Pseudorehearsal (a method developed by Robins [19] to solve the catastrophic forgetting problem which arises from the excessive plasticity of neural networks) is significantly more effective than other local learning algorithms for the sequential task. We further consider the concept of local learning and suggest that pseudorehearsal is so effective because it works directly at the level of the learned function, and not indirectly on the representation of the function within the network. We also briefly explore the effect of local learning on generalisation within the task.

Artificial neural networks for analyzing inter-limb coordination: The golf chip shot

Motor control research relies on theories, such as coordination dynamics, adapted from physical sciences to explain the emergence of coordinated movement in biological systems. Historically, many studies of coordination have involved inter-limb coordination of relatively few degrees of freedom. This study looked at the high-dimensional inter-limb coordination used to perform the golf chip shot toward six different target distances. This study also introduces a visualization of high-dimensional coordination relevant within the coordination dynamics theoretical framework. A specific type of Artificial Neural Network (ANN), the Self-Organizing Map (SOM), was used for the analysis. In this study, the trajectory of consecutive best-matching nodes on the output map was used as a collective variable and subsequently fed into a second SOM which was used to create visualization of coordination stability. The SOM trajectories showed changes in coordination between movement patterns used for short chip shots and movement patterns used for long chip shots. The attractor diagrams showed non-linear phase transitions for three out of four players. The methods used in this study may offer a solution for researchers from a coordination dynamics perspective who intend to use data obtained from discrete high-dimensional movements.