Cognitive Models and Intelligent Environments for Learning Programming

1. Front Matter

   Pages I-VIII

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2. Introduction

   1. Introduction

      • Enrica Lemut, Benedict du Boulay, Giuliana Dettori

      Pages 1-5

3. Understanding Programming

   1. General Theory of Programming Knowledge

      1. Task Analysis and Cognitive Model as a Framework to Analyse Environments for Learning Programming

         • Janine Rogalski, Renan Samurçay

         Pages 6-19

      2. Mental Representations of Computer Languages — a Lesson from Practice

         • Gerrit C. van der Veer

         Pages 20-33

      3. Towards a Unified Model of Learning to Program

         • Peter Pirolli

         Pages 34-48

      4. Acquiring Experience in Object-Oriented Programming: Effects on Design Strategies

         • Françoise Détienne

         Pages 49-58

      5. Programming and Design

         • John Sumiga

         Pages 59-70

   2. Theory of Particular Aspects of Programming Knowledge

      1. Program Comprehension Skills and Their Acquisition: A Call for an Ecological Paradigm

         • Barbee Mynatt Teasley

         Pages 71-79

      2. A Distributed Model of Cognitive Behaviour in Specification Understanding

         • Francisco C. Simplicio-Filho

         Pages 80-93

      3. How Confirmation Bias Affects Novice Programmers in Testing and Debugging: Research Strategies and Implications for Tools

         • Laura Marie Leventhal

         Pages 94-102

      4. The “Private” Relation of the Student with Systematic Programming and Validation

         • Vassilios Dagdilelis

         Pages 103-113

4. Environments for Learning Programming

   1. Intelligent Tutoring Systems

      1. Towards an Intelligent Environment for Learning Introductory Programming

         • Peter L. Brusilovsky

         Pages 114-124

      2. Programming Environments for Novices

         • Haider Ramadhan, Benedict du Boulay

         Pages 125-134

      3. Student Modeling in an Intelligent Programming Tutor

         • Albert T. Corbett, John R. Anderson

         Pages 135-144

      4. Incidental Reification of Goals in an Intelligent Tutor for Smalltalk

         • Mark K. Singley, John M. Carroll, Sherman R. Alpert

         Pages 145-155

      5. Methodology and Design Issues in Capra, an Environment for Learning Program Construction

         • M. Felisa Verdejo, Isabel Fernández, M. Teresa Urretavizcaya

         Pages 156-171

      6. Linking Theory with ITS Implementation: Models of Programming and the Development of Programming Tutors

         • Simon P. Davies, Adrian M. Castell

         Pages 172-184

   2. Pure Programming Environments

      1. Two and One-Half Approaches to Helping Novices Learn Recursion

         • Gordon I. McCalla, Jim E. Greer

         Pages 185-197

      2. Knowledge Based Tools in Software Engineering Education

         • Paola Forcheri, Maria Teresa Molfino

         Pages 198-209

      3. Analogies in an Intelligent Programming Environment for Learning LISP

         • Gerhard Weber

         Pages 210-219

At present, there is a general consensus on the nature of learning programming, but there are different opinions on what forms an effective environment for it. It is generally recognized that the development of a mental model is a formidable task for the student and that learning programming is a complex activity that depends heavily on metacognitive skills. This book, based on a NATO workshop, presents both pure cognitive models and experimental learning environments, and discusses what characteristics can make a learning model effective, especially in relation to the learning environment (natural or computerized). The papers cover cognitive models related to different aspects of programming, classes of learners, and types of environment, and are organized in three groups: theoretical and empirical studies on understanding programming, environments for learning programming, and learning programming in school environments. Comprehension, design, construction, testing, debugging, and verification are recognized as interdependent skills, which require complicated analysis and may develop independently, and indifferent orders, in novices. This book shows that there is unlikely to be asingle path from novice to expert and that the structure of the final product (the program) may not constrain the process by which it comes into being as much as some would advocate.

• Cognitive Models
• Intelligent Environments
• Intelligente Programmierumgebungen
• Kognitive Modelle
• Learning Environments
• Learning Programming
• Programmierausbildung
• algorithm
• algorithms
• knowledge
• knowledge base
• learning
• modeling
• search strategy
• verification

• Istituto Matematica Applicata, C.N.R., Genova, Italy

  Enrica Lemut, Giuliana Dettori

• School of Cognitive and Computing Sciences, University of Sussex, Falmer, Brighton, UK

  Benedict Boulay

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