Abstract—Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without

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 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? 1.

Contents 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 teaching in CS1 2.3.1 Goals and progress 2.3.2 Course design and teaching methods 2.3.3 Alternative methods and curricula 2.4 Summary 3.0 A study of an introductory programming paper 3.1 The design of COMP103 3.1.1 Context 3.1.2 Lectures and knowledge 3.1.3 Laboratory sessions and strategy 3.1.4 Summary 3.2 The study 3.2.1 Background 3.2.2 Method 3.3 Results 3.3.1 Lab based problem tallies 3.3.2 Trends 3.3.3 Other observations 4.0 Discussion 4.1 Kinds of novice 4.2 Knowledge, strategies, and effective teaching and learning 4.3 A framework 5.0 Summary References

Workplace learning has developed as a field both of practice and of research over the past decade. The increase in interest is due in part to heightened awareness that workplace knowledge and skills contribute to enterprise and national competitiveness, but it is also due to an increased focus on the connections to be made between theory and practice as part of an education or training experience. At the same time, new learning technologies have enhanced delivery of instruction and learning materials in workplaces. This article reviews some of the conceptualizations of workplace learning and its cognitive bases. It also examines workplaces as learning environments and considers the special challenges involved in the flexible delivery of training to workplaces.

instructional design. Undergraduate students often start their academic course of studies with inadequate learning and thinking skills. Our college has a policy of setting high standards and demands, while supporting students ' learning in a variety of ways. In this paper we present a distinctive course designed to aid students develop algorithmic problem-solving skills. The course is taught in parallel to a CS1 course and elaborates on activities such as analogical reasoning, prototyping problems, comparison between alternative solutions and reflection on problem-solving processes. It is one of the courses offered at our institution aimed at strengthening general learning and thinking skills, in addition to the regular disciplinary curriculum. Feedback from participants in the course demonstrates a developed awareness and appreciation of abstract ideas beyond programming knowledge. Students report on acquiring problem-solving skills that enable them to cope with compound problems. Additionally, students claimed that they had broadened their repertoire of algorithmic ideas leading to more efficient and elegant solutions. 1.