Automatic extraction of notions from course material (original) (raw)
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Lecture Notes in Computer Science, 2012
Computer-based educational approaches provide valuable supplementary support to traditional classrooms. Among these approaches, intelligent learning systems provide automated questions, answers, feedback, and the recommendation of further resources. The most difficult task in intelligent system formation is the modelling of domain knowledge, which is traditionally undertaken manually or semi-automatically by knowledge engineers and domain experts. However, this error-prone process is time-consuming and the benefits are confined to an individual discipline. In this paper, we propose an automated solution using lecture notes as our knowledge source to utilise across disciplines. We combine ontology learning and natural language processing techniques to extract concepts and relationships to produce the knowledge representation. We evaluate this approach by comparing the machine-generated vocabularies to terms rated by domain experts, and show a measurable improvement over existing techniques.
Within the framework of course-assisted creation, we are developing a prototype whose goal will be to enrich an ontology of teaching concepts. To do that, we automatically query search engines with key words extracted from pages that were previously analysed (beginning with an ontology which treats on a hierarchical basis the first keywords). This analysis (lexical, syntactic and semantic), targeted towards the extraction of definitions ("this concept-X is a ...etc.") and of specializations ("this concept-X comes in ...etc."), is based on the exploitation of a base of syntactic templates automatically learned from the analysis of definitions of several on-line dictionaries, and from a minimal dic- tionary (in particular to treat synonymies). The ontology thus created finally generates a course draft that must be "finalized" manually, in spite of the use of a few subroutines for knowledge synthesis (for the moment our work is mainly focused on the enrich...
Instructional Material Development using Ontology Learning
Social and Management Research Journal, 2018
In a university setting, lecturers are instructional designers responsible todesign and develop instructional materials to be used in class. Textbooksand presentation slides are among the sources used in the delivery ofknowledge. However, in order to facilitate different students’ learning styles,alternatives for textbooks must be considered, allowing the course contentto be organised in smaller chunks. This paper describes the developmentontology process using ontology learning technique. Ontology is a set ofknowledge which contains objects, concepts, entities, and the relationshipsamong them in a particular domain. In this study, the course chosen toanalyse the development of ontology is Fundamentals of Computer Science(CSC401). This is an introductory course taken by students during semesterone in the Faculty of Computer and Mathematical Sciences. Textbook is thesource used in developing this ontology. The technique chosen in this studyis a semi-automatic ontology development tha...
Extracting learning concepts from educational texts in intelligent tutoring systems automatically
Expert Systems with Applications, 2010
This paper argues that the educational support systems can give a meaning to an educational content semantically, and an answer has been sought to the question of ''what should be taught to students" in the field of intelligent tutoring systems. With reference this aim, a system, which automatically detects the concepts to be learned by students, has been designed. The developed system uses the statistical language models together with conceptual map modeling as a student model to extract the minimal set of learning concepts within an educational content. In the study, ten corpora have been generated as a learning domain, which consist of two different subjects in mathematics. For each subject, five distinct chapters have been quoted from the books written by various authors. After extracting the candidate concepts from the given content, the system checks to clarify whether these candidates are in a dictionary within postprocessing. The dictionary consists of approximately 9500 technical terms related to the learning domain. The system performance has also been analyzed using Recall, Precision and F-measure scores. The results indicate that the postprocessing step increases precision with a small loss of recall.
Helping Courseware Authors to Build Ontologies: The Case of TM4L
2007
The authors of topic map-based learning resources face major difficulties in constructing the underlying ontologies. In this paper we propose two approaches to address this problem. The first one is aimed at automatic construction of a "draft" topic map for the authors to start with. It is based on a set of heuristics for extracting semantic information from HTML documents and transforming it into a topic map format. The second one is aimed at providing help to authors during the topic map creating process by mining the Wikipedia knowledge base. It suggests "standard" names for the new topics (paired with URIs), along with lists of related topics in the considered domain. The proposed approaches are implemented in the educational topic maps editor TM4L.
Ontology Engineering for Concept-based Courseware Authoring
Abstract The goal of ontological engineering is effective support of ontology development and use throughout its life cycle. In this paper we present our view on applying ontology engineering for concept-based courseware authoring, which is an elaboration of our approach to knowledge classification and indexing in a Web-based course environment aimed at assisting students in retrieving, evaluating, and comprehending information when performing open-ended learning tasks.
Using Ontologies to generate Learning Objects automatically
In this paper, the possibilities and challenges of using well known ontologies like WordNet and YAGO as knowledge bases in order to automatically compose learning objects for specific e-learning applications are explored. This approach takes advantage of those existing and free knowledge bases, combining them with concept level entities which give an outline of the course. A software system is presented which generates interactive exercises in HTML format for different courses. Based on an exercise previously generated by hand as a pattern and the already mentioned ontologies as knowledge bases, the system SIEG (System for Interactive Exercises Generation) allows creating new versions of the exercise changing its specific content. After creation, the exercises are transformed into learning objects by the AGORA learning object management platform.
Semi-Automatic Ontology Design for Educational Purposes
Pattern and Data Analysis in Healthcare Settings, 2000
In this paper, we present a (semi) automatic framework that aims to produce a domain concept from text and to derive domain ontology from this concept. This paper details the steps that transform textual resources (and particularly textual learning objects) into a domain concept and explains how this abstract structure is transformed into more formal domain ontology. This methodology targets particularly the educational field because of the need of such structures (Ontologies and Knowledge Management). The paper also shows how these structures make it possible to bridge the gap between core concepts and Formal ontology.
Journal of Intelligent & Fuzzy Systems, 2018
Course sequencing plays a major role in Intelligent Tutoring Systems because it determines the learning path of the student. However, it is difficult to define this order during early stages when there is no interaction with the student. The objective of this study is to determine the sequence of learning concepts considering an ontology and Wikipedia information. We used a text mining algorithm using Wikipedia to determine course sequencing. The knowledge base is formed by concepts and relationships in an ontology, in addition to Wikipedia articles of the same concepts. To evaluate the accuracy of the algorithm, we made a comparison against domain experts. According to the Pearson test, a correlation of 0.664 between the algorithm and experts was obtained, with a confidence level higher than 99%. The learning sequence can be defined with this method when we do not have evidence of student knowledge, to be later modified according to the interaction of the student.
2009
This paper presents a semi-automatic framework that aims to produce domain concept maps from text and then to derive domain ontologies from these concept maps. This methodology particularly targets the eLearning and AIED (Artificial Intelligence in Education) communities as they need such structures to sustain the production of eLearning resources tailored to learners’ needs. This paper details the steps to transform textual resources, particularly textual learning objects (LOs), into domain concept maps and it explains how this abstract structure is transformed into a formal domain ontology. A methodology is also presented to evaluate the results of ontology learning. The paper shows how such structures (domain concept maps and formal ontologies) make it possible to bridge the gap between eLearning and Intelligent Tutoring Systems by providing a common domain model.