102 research outputs found
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
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
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
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