Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor

An Integrated Approach for Automatic\ud Aggregation of Learning Knowledge Objects

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

A Synergic Neuro-Fuzzy Evaluation System in Cultural Intelligence

In today’s age of globalization, cultural awareness has become a challenge for designers of tutoring systems to include the cultural dimension in the tutoring strategy and in the learning environment. Nevertheless, cultural awareness is also a domain to be learned by a student, and a competency that can be assessed. Research on cultural intelligence has provided a new perspective and presented a new way to alleviate issues arising from cross-cultural education. To date, no research on cultural intelligence has been empirically computerized with soft-computing technology. This research aims to invent a cultural intelligence computational model and to implement the model in an expert system through the use of artificial intelligence technology. The purpose of this study is to provide intercultural training for individuals to solve the intercultural adaptation problems they may be faced with in a variety of authentic crosscultural situations

Planning gamification strategies based on user characteristics and DM : a gender-based case study.

Gamification frameworks can aid in gamification planning for education. Most frameworks, however, do not provide ways to select, relate or recommend how to use game elements, to gamify a certain educational task. Instead, most provide a "one-size-fits-all" approach covering all learners, without considering different user characteristics, such as gender. Therefore, this work aims to adopt a data-driven approach to provide a set of game element recommendations, based on user preferences, that could be used by teachers and instructors to gamify learning activities. We analysed data from a novel survey of 733 people (male=569 and female=164), collecting information about user preferences regarding game elements. Our results suggest that the most important rules were based on four (out of nineteen) types of game elements: Objectives, Levels, Progress and Choice. From the perspective of user gender, for the female sample, the most interesting rule associated Objectives with Progress, Badges and Information (confidence=0.97), whilst the most interesting rule for the male sample associated also Objectives with Progress, Renovation and Choice (confidence=0.94). These rules and our descriptive analysis provides recommendations on how game elements can be used in educational scenarios.Comment: https://drive.google.com/file/d/1UI28N2UtrOfL06k2mzHIUdPcgQtdfmy9/view?usp=sharin

Roger Nkambou. Modélisation des connaissances de la matière dans un Système Tutoriel Intelligent : modèles, outils et applications. Thèse de PhD en Informatique, Université de Montréal (Canada), 5 juin 1996

Nkambou Roger. Roger Nkambou. Modélisation des connaissances de la matière dans un Système Tutoriel Intelligent : modèles, outils et applications. Thèse de PhD en Informatique, Université de Montréal (Canada), 5 juin 1996. In: Sciences et techniques éducatives, volume 3 n°2, 1996. pp. 278-279

Explicit Reflection in Prolog-Tutor

Explicit Reflection in Prolog-Tutor. This paper describes a reflection-based approach for open learner modeling (OLM). Tutoring dialogues are used by learners to explicitly reveal their own knowledge state to themselves. Dewey's theory of reflective thinking is used to create tutorial strategies which govern these dialogues. Drake's specification of critical thinking, associated to a defined set of skills, is used to define tutoring tactics implementing these strategies. The main contribution of this approach to OLM is that it provides a set of principled and reusable tutorial strategies and tactics to promote reflection, as they are based on domain independent theories. Furthermore, an evaluation of such a principled approach to OLM is straightforward in certain cases, as it refers to theories which already provide evaluation criteria. The approach is integrated in Prolog-Tutor, an existing intelligent tutoring system for Logic Programming. This paper presents a qualitative study of the resulting system, based on think-aloud protocols. A result analysis reveals that explicitly fostering reflection supports reflection based OLM and provides landmarks to explain its manifestations. However, the results also suggest that this openness may be less helpful when used by learners who have already honed a high level of proficiency in logic programming

Roger Nkambou. Modélisation des connaissances de la matière dans un Système Tutoriel Intelligent : modèles, outils et applications. Thèse de PhD en Informatique, Université de Montréal (Canada), 5 juin 1996

Nkambou Roger. Roger Nkambou. Modélisation des connaissances de la matière dans un Système Tutoriel Intelligent : modèles, outils et applications. Thèse de PhD en Informatique, Université de Montréal (Canada), 5 juin 1996. In: Sciences et techniques éducatives, volume 3 n°2, 1996. pp. 278-279