Extracting Dependency Relations from Digital Learning Content (original) (raw)
2019
English. This paper presents a method for prerequisite learning classification between educational concepts. The proposed system was developed by adapting a classification algorithm designed for sequencing Learning Objects to the task of ordering concepts from a computer science textbook. In order to apply the system to the new task, for each concept we automatically created a learning unit from the textbook using two criteria based on concept occurrences and burst intervals. Results are promising and suggest that further improvements could highly benefit the results. 1 Italiano. Il presente articolo descrive una stategia per l'identificazione di prerequisiti fra concetti didattici. Il sistema propostoè stato realizzato adattando un algoritmo per ordinamento di Learning Objects al compito di ordinamento di concetti estratti da un libro di testo di informatica. Per adeguare il sistema al nuovo scenario, per ogni concetto stata automaticamente creata una unità di apprendimento a partire dal libro di testo selezionando i contenuti sulla base di due differenti criteri: basandosi sull'occorrenza del concetto e sugli intervalli di burst. I risultati sono promettenti e lasciano intuire la possibilità di ulteriori miglioramenti.
The learning process for a user becomes seriously restrictive in trying to discover the relationships between concepts and in searching for a part of concept (called an object) such as a solved example illustrating the concept or the application of the concept. This paper presents the theory for building relational graph that depicts how concepts are linked to each other. By selecting to zoom on a particular concept, the user's view changes to conceptual graph where he can view and access all the objects related to a particular concept. Rule-based algorithms are presented to identify objects of a concept, to determine concept boundary and to build the trees. The lecture material on Algorithms course from MIT is used for experimentation of the ideas. In addition to efficient searching for desired topic, the system also enhances the understanding and the learning of the user.
Building Learning Objects from Electronic Documents
Knowledge reuse has become a main research matter in the Education Technology area. Learning Objects provide a means to promote knowledge reuse, but their success is conditioned by the usefulness of their associated metadata. The annotation of metadata is a hard task that should be eased. This paper presents an approach to semi automatically generate and annotate Learning Objects from electronic documents.
Automatic Generation of Contents Models for Digital Learning Materials
There has been much research that demonstrates the effectiveness of using ontology to support the construction of knowledge during the learning process. However, the widespread adoption in classrooms of such methods are impeded by the amount of time and effort that is required to create and maintain an ontology by a domain expert. In this paper, we propose a method to automatically generate a contents model by analyzing learning materials with the aim of supporting the construction of knowledge structures. A map of the keyword nodes is constructed by applying text mining techniques to find the important words and phrases and their relations contained within the learning materials. The process retains links between the nodes and the original learning materials, and it is therefore possible to recommend and rank sections that cover a concept contained within the contents model map.
Relation extraction among learning concepts in intelligent tutoring systems
2009 International Conference on Application of Information and Communication Technologies, 2009
This paper addresses the question of how to extract the relevance among the learning concepts in an intelligent tutoring system using the mathematical modeling of the search engines. To test the proposed approach, two learning domains have been selected from mathematics. For each domain, five distinct chapters have been quoted from the books written by various authors. After extracting candidate concepts, some feature of them have been determined. After feature extraction, the relationships among the concepts have been detected using context vector models, and finally, the concept maps have been automatically constructed as maximum spanning tree.
Mining Prerequisite Relationships Among Learning Objects
Communications in Computer and Information Science, 2016
The process of carefully choosing and sequencing a set of Learning Objects (LOs) to build a course may reveal to be quite a challenging task. In this work we focus on an aspect of such challenge, related to the verification and respect of the relationships of pedagogical dependence that holds between two LOs added to a course (meaning that if a given LO has another one as "pre-requisite", then any sequencing of the LOs in the course will need to have the latter LO taken by the learners before of the former). An innovative Machine learning-based approach for the identification of these kinds of relationships is proposed.
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.
Inferring Concept Prerequisite Relations from Online Educational Resources
Proceedings of the AAAI Conference on Artificial Intelligence
The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course...
Automatic extraction of notions from course material
2008
Formally defining the knowledge units taught in a course helps instructors ensure a sound coverage of topics and provides an objective basis for comparing the content of two courses. The main issue is to list and define the course concepts, down to basic knowledge units. Ontology learning techniques can help partially automate the process by extracting information from existing materials such as slides and textbooks. The TrucStudio course planning tool, discussed in this article, provides such support and relies on Text2Onto to extract concepts from course material. We conducted experiments on two different programming courses to assess the quality of the results.