Nathan Rountree

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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 objectoriented versus procedural programming. (We do not cover research relating specifically to other(More)
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(More)
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(More)
We present the results of a survey that focuses on the backgrounds and expectations of a group of CS1 students in the first weeks of semester. When comparing their survey answers to their final grades on the course, we saw some surprising things: the group which indicated an intention to continue in computer science did no better than any other, and the(More)
Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large number of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the(More)
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(More)
1.0 Introduction 2.0 Learning to program 2.1 Overview 2.1.1 Experts vs. novices 2.1.2 Knowledge vs. strategies 2.1.3 Comprehension vs. generation 2.1.4 Procedural vs. object–oriented 2.1.5 Other 2.2 Novice programmers 2.2.1 The task 2.2.2 Mental models and processes 2.2.3 Novice capabilities and behavior 2.2.4 Kinds of novice 2.3 Novice learning and(More)
Two issues of related interest are investigated in this paper. The first issue is associated with the statement that “Learning to program is a key objective in most introductory computing courses, yet many computing educators have voiced concern over whether their students are learning the necessary programming skills in those courses” (McCracken et al.(More)
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(More)