Abstract: This paper discusses the problems of developing versatile adaptive and intelligent learning systems that can be used in the context of practical Web-based education. We argue that versatility is an important feature of successful Web-based education systems. We introduce ELM-ART, an intelligent interactive educational system to support learning programming in LISP. ELM-ART provides all learning material online in the form of an adaptive interactive textbook. Using a combination of an overlay model and an episodic student model, ELM-ART provides adaptive navigation support, course sequencing, individualized diagnosis of student solutions, and example-based problem-solving support. Results of an empirical study show different effects of these techniques on different types of users during the first lessons of the programming course. ELM-ART demonstrates how some interactive and adaptive educational components can be implemented in WWW context and how multiple components can be naturally integrated together in a single system.

Active learning is an effective learning approach. In this paper, we presentanintelligentagent assisted environment for active learning. The system is to better support studentcentered, self-paced, and highly interactive learning approach. Students' learning-related profiles, such as learning styles and background knowledge, are used in selecting, organizing, and presenting learning materials. A new approachto course content organization and delivery is being developed based on smart instructional components, whichcanbeintegrated into a wide range of courses. The system is being implemented using the prevalentInternet, Web, digital library, and multi-agent technologies. Keywords: active learning, Web-based education, multiagent system, XML 1.

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This paper describes a novel editor intended as an aid in the learning of the functional programming language Standard ML. A common technique used by novices is programming by analogy whereby students refer to similar programs that they have written before or have seen in the course literature and use these programs as a basis to write a new program. We present a novel editor for ML which supports programming by analogy by providing a collection of editing commands that transform old programs into new ones. Each command makes changes to an isolated part of the program. These changes are propagated to the rest of the program using analogical techniques. We observed a group of novice ML students to determine the most common programming errors in learning ML and restrict our editor such that it is impossible to commit these errors. In this way, students encounter fewer bugs and so their rate of learning increases. Our editor, C Y NTHIA, has been implemented and is due to be tested on st...

Functional programming presents new challenges in the design of programming environments. In a strongly typed functional language, such as ML, much conventional debugging of runtime errors is replaced by dealing with compile time error reports. On the other hand, the cleanness of functional programming opens up new possibilities for incorporating sophisticated correctness-checking techniques into such environments. C Y NTHIA is a novel editor for ML that both addresses the challenges and explores the possibilities. It uses an underlying proof system as a framework for automatically checking for semantic errors such as non-termination. In addition, C Y NTHIA embodies the idea of programming by analogy --- whereby users write programs by applying abstract transformations to existing programs. This paper investigates C Y NTHIA's potential as a novice ML programming environment. We report on two studies in which it was found that students using C Y NTHIA commit fewer er...

This paper examines the learning of the functional programming language Standard ML. A common technique used by novices is programming by analogy whereby students refer to similar programs that they have written before or have seen in the course literature and use these programs as a basis to write a new program. We present a novel editor for ML which supports programming by analogy by providing a collection of editing commands that transform old programs into new ones. Each command makes changes to an isolated part of the program. These changes are propagated to the rest of the program using analogical techniques. Many commands are at a level high enough to provide guidance to the novice during program development. We observed a group of novice ML students to determine the most common programming errors in learning ML and restrict our editor such that it is impossible to commit these errors. In this way, students encounter fewer bugs and so their rate of learning increases.

Abstract. We present a computational framework designed to provide adaptive support aimed at triggering learning from problem-solving activities in the presence of worked-out examples. The key to the framework’s ability to provide this support is a user model that exploits a novel classification of similarity to infer the impact of a particular example on a given student’s metacognitive behaviors and subsequent learning. 1

Introduction This note reports on an evaluation of my ML editor, C Y NTHIA, carried out in Oct/Nov 1997 at Napier University. The aim was to try to give answers to the following questions: 1. Does the use of C Y NTHIA make easier the implementation of ML functions for novice ML users? 2. What, if any, educational advantages are there to using C Y NTHIA? The main focus here is to provide an answer to question (1). I will also address (2) to a lesser extent. The study reported here should be seen as an exploratory one -- many questions have been thrown up that cannot be answered at the present time. 2 Experimental Setup The students using C Y NTHIA were 40 postgraduates following the Software Technology course at Napier University. The course is taught by Andrew Cumming and organised as follows. Students are given one lectur

We present an intelligent tutoring framework designed to help students acquire problem-solving skills from pedagogical activities involving worked-out example solutions. Because of individual differences in their meta-cognitive skills, there is great variance in how students learn from examples. Our framework takes into account these individual differences and provides tailored support for the application of two key meta-cognitive skills: self-explanation (i.e., generating explanations to oneself to clarify studied material) and min-analogy (i.e., not relying too heavily on examples during problem solving). We describe the framework’s two components. One component explicitly scaffolds self-explanation during example studying with menu-based tools and direct tailored tutorial interventions, including the automatic generation of example solutions at varying degrees of detail. The other component supports both self-explanation and min-analogy during analogical problem solving by relying on subtler scaffolding, including a highly innovative example selection mechanism. We conclude by reporting results from an empirical evaluation of the former component, showing that it facilitates cognitive skill acquisition when students access it at the appropriate learning stage.