Roger Nkambou

Advances in Intelligent Tutoring Systems

The idea for this book on Intelligent Tutoring Systems (ITS) was sparked by the success of the ITS08 international conference. The number of presentations and their quality bore witness to the vitality and maturity of the field, and the enthusiasm of the participants held out a promise of sustainability and innovative research. Long life to ITS research! The book is divided into five parts. The introductory chapters to these parts, which summarize foundations, developments, strengths and weaknesses in each of the areas covered, are addressed to all readers. For those who want more in-depth knowledge, we give the floor to researchers who present their work, their results, and their view of what the future holds. It is our hope that all readers will find the book informative and thought-provoking.

Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project

One of the goals of the knowledge puzzle project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the knowledge puzzle ontology learning tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a three-dimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.

RuleGrowth: mining sequential rules common to several sequences by pattern-growth

Mining sequential rules from large databases is an important topic in data mining fields with wide applications. Most of the relevant studies focused on finding sequential rules appearing in a single sequence of events and the mining task dealing with multiple sequences were far less explored. In this paper, we present RuleGrowth, a novel algorithm for mining sequential rules common to several sequences. Unlike other algorithms, RuleGrowth uses a pattern-growth approach for discovering sequential rules such that it can be much more efficient and scalable. We present a comparison of RuleGrowths performance with current algorithms for three public datasets. The experimental results show that RuleGrowth clearly outperforms current algorithms for all three datasets under low support and confidence threshold and has a much better scalability.

An Integrated Approach for Automatic Aggregation of Learning Knowledge Objects

This paper presents the Knowledge Puzzle, an ontology-based platform designed to facilitate domain knowledge acquisition from textual documents for knowledge-based systems. First, the Knowledge Puzzle Platform performs an automatic generation of a domain ontology from documents’ content through natural language processing and machine learning technologies. Second, it employs a new content model, the Knowledge Puzzle Content Model, which aims to model learning material from annotated content. Annotations are performed semi-automatically based on IBM’s Unstructured Information Management Architecture and are stored in an Organizational memory (OM) as knowledge fragments. The organizational memory is used as a knowledge base for a training environment (an Intelligent Tutoring System or an e-Learning environment). The main objective of these annotations is to enable the automatic aggregation of Learning Knowledge Objects (LKOs) guided by instructional strategies, which are provided through SWRL rules. Finally, a methodology is proposed to generate SCORM-compliant learning objects from these LKOs.

Anytime dynamic path-planning with flexible probabilistic roadmaps

Probabilistic roadmaps (PRM) have been demonstrated to be very promising for planning paths for robots with high degrees of freedom in complex 3D workspaces. In this paper we describe a PRM path-planning method presenting three novel features that are useful in various real-world applications. First, it handles zones in the robot workspace with different degrees of desirability. Given the random quality of paths that are calculated by traditional PRM approaches, this provides a mean to specify a sampling strategy that controls the search process to generate better paths by simply annotating regions in the free workspace with degrees of desirability. Second, our approach can efficiently re-compute paths in dynamic environments where obstacles and zones can change shape or move concurrently with the robot. Third, it can incrementally improve the quality of a generated path, so that a suboptimal solution is available when required for immediate action, but get improved as more planning time is affordable

A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems

Domain experts should provide relevant domain knowledge to an Intelligent Tutoring System (ITS) so that it can guide a learner during problem-solving learning activities. However, for many ill-defined domains, the domain knowledge is hard to define explicitly. In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions, and how it can support tutoring services during problem-solving exercises. This article describes an extension of this approach to extract a problem space that is richer and more adapted for supporting tutoring services. We combined sequential pattern mining with (1) dimensional pattern mining (2) time intervals, (3) the automatic clustering of valued actions and (4) closed sequences mining. Some tutoring services have been implemented and an experiment has been conducted in a tutoring system.

A Survey of Domain Ontology Engineering: Methods and Tools

With the advent of the Semantic Web, the field of domain ontology engineering has gained more and more importance. This innovative field may have a big impact on computer-based education and will certainly contribute to its development. This chapter presents a survey on domain ontology engineering and especially domain ontology learning. The chapter focuses particularly on automatic methods for ontology learning. It summarizes the state of the art in natural language processing techniques and statistical and machine learning techniques for ontology extraction. It also explains how intelligent tutoring systems may benefit from this engineering and talks about the challenges that face the field.

CREAM-Tools: An Authoring Environment for Knowledge Engineering in Intelligent Tutoring Systems

This chapter presents an authoring model and a system for curriculum development in Intelligent Tutoring Systems (ITSs). We first present an approach for modeling knowledge of the subject matter (the curriculum) to be taught by a large-scale ITS, and we show how it serves as the framework of the authoring process. This approach, called CREAM (Curriculum REspresentation and Acquisition Model), allows creation and organization of the curriculum according to three models concerning respectively the domain, the pedagogy and the didactic aspects. The domain is supported by the capability model (CREAM-C) which represents and organizes domain knowledge through logical links. The pedagogical view allows the definition and organization of teaching objectives by modeling skills required to achieve them and evaluating the impact of this achievement on the domain knowledge (CREAM-O and pedagogical model). The didactic component is based on a model of resources which defines and specifies different activities that are necessary to support teaching (CREAM-R). The construction of each part of CREAM is supported by specific authoring tools and methods. The overall authoring system, called CREAM-Tools, allows Instructional Designers (IDs) to produce a complete ITS curriculum based on the CREAM approach. Although this article is limited to curriculum development, we give some guidelines on how the resulting system could support the construction of other ITS components such as the planner and the student model.

Building Intelligent Tutoring Systems for Ill-Defined Domains

Domains in which traditional approaches for building tutoring systems are not applicable or do not work well have been termed “ill-defined domains.” This chapter provides an updated overview of the problems and solutions for building intelligent tutoring systems for these domains. It adopts a presentation based on the following three complementary and important perspectives: the characteristics of ill-defined domains, the approaches to represent and reason with domain knowledge in these domains, and suitable teaching models. Numerous examples are given throughout the chapter to illustrate the discussion.

Hierarchical Representation and Evaluation of the Student in an Intelligent Tutoring System

In this paper, we present an approach to hierarchical knowledge representation for the students evaluation in propositional logic. The hierarchical evaluation consists in assessing the students state of knowledge at several levels of granularity. The relevance of the method is justified by the need for a precise and flexible diagnosis of the learners skills in a given domain. For that purpose, we shall model the propagation of the evaluation from a specific level of knowledge content to more general levels, using Bayesian inferences and neural networks classifications.