Nathan Rountree

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?

Finding sporadic rules using apriori-inverse

We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate itemsets above a maximum support threshold. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). We show that Apriori-Inverse finds all perfectly sporadic rules much more quickly than Apriori. We also propose extensions to Apriori-Inverse to allow us to find some (but not necessarily all) imperfectly sporadic rules.

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.

Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection

The growing complexity and volume of modern databases make it increasingly important for researchers and practitioners involved with association rule mining to make sense of the information they contain. Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection provides readers with an in-depth compendium of current issues, trends, and technologies in association rule mining. Covering a comprehensive range of topics, this book discusses underlying frameworks, mining techniques, interest metrics, and real-world application domains within the field.Association rules are an intuitive descriptive paradigm that has been used extensively in later years and in different application domains with the purpose to identify the regularities and correlation in a set of observed objects. However, recently, association rules’ statistical measures (support and confidence) have been criticized because in some cases have shown to fail their primary goal that is to select the most relevant and significant association rules. In this paper we propose a new model that replaces the support measure. The new model, like support, is a tool for the identification of the reliable rules and is used also to reduce the traversal of the itemsets search space. The proposed model adopts new criteria in order to establish the reliability of the information extracted from the database. These criteria are based on Bayes’ Theorem and on an estimate of the probability density function of each itemset. According to our criteria, the information that we have obtained from the database on an itemset is reliable if and only if the confidence interval of the estimated probability is low compared with the most likely value of it. We will see how this method can be computed in an approximated way, but satisfactory, with computational time comparable to the test on support

Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse

Discovering association rules efficiently is an important data mining problem. We define sporadic rules as those with low support but high confidence; for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. To alleviate this problem, we propose a new method of discovering sporadic rules without having to produce all other rules above the minimum support threshold. The new method, called Apriori-Inverse, is a variation of the Apriori algorithm that uses the notion of maximum support instead of minimum support to generate candidate itemsets. Candidate itemsets of interest to us fall below a maximum support value but above a minimum absolute support value. Rules above maximum support are considered frequent rules, which are of no interest to us, whereas rules that occur by chance fall below the minimum absolute support value. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). This article is an expanded version of Koh and Rountree (2005).

Elaborating on threshold concepts

We propose an expanded definition of Threshold Concepts (TCs) that requires the successful acquisition and internalisation not only of knowledge, but also its practical elaboration in the domains of applied strategies and mental models. This richer definition allows us to clarify the relationship between TCs and Fundamental Ideas, and to account for both the important and the problematic characteristics of TCs in terms of the Knowledge/Strategies/Mental Models Framework defined in previous work.

Predictors For success in studying CS

Nathan Rountree Department of Computer Science University of Otago PO Box 56, Dunedin, New Zealand rountree@cs.otago.ac.nz Tamar Vilner Computer Science Department Open University of Israel 16 Klausner Street,Tel Aviv, Israel 61392 tami@cs.openu.ac.il Brenda Cantwell Wilson Dept of Computer Science and Information Systems Murray State University 652 Business Building South Murray, KY, USA 42071-3314 Brenda.Wilson@murraystate.edu Roger Boyle School of Computing University of Leeds Leeds, LS2 9JT, UK roger@comp.leeds.ac.uk

Virtual Aggregated Processor in Multi-core Computers

Parallel computing has been in the spotlight with the advent of multi-core computers. The popular multithreading model does not scale very well when there are hundreds or thousands of cores, since it can only help exploit coarse-grained parallelism. There exist a lot of fine-grained parallelism to be exploited in I/O tasks and memory accesses during execution of a thread. Our counter-Amdahls law tells us that it is more effective to parallelize the serial fraction of a parallel algorithm rather than the parallelized fraction in order to maximize the speedup. In this paper, we have proposed a virtual aggregated processor that is aiming at speeding up execution of a thread through exploiting the fine-grained parallelism in I/O tasks and memory accesses. We have proposed and implemented two techniques, helper thread and I/O specialization, to demonstrate the potential effectiveness of the virtual aggregated processor technology.