ErauzOnt: A Framework for Gathering Learning Objects from Electronic Documents

Larrañaga, Mikel; Calvo, Iñaki; Elorriaga, Jon A.; Arruarte, Ana; Verbert, Katrien; Duval, Erik

doi:10.1109/icalt.2011.209

Cited by 7 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LDO will all Intelligent Tutoring S sessions, or the student learning process in e might allow the stude learnt and provide hint followed to prevent get • LOs Gathering: At th examples, exercises, learning process are id generation process he ErauzOnt [5]. The L process is used to cond LOs Gathering task is d Both the acquisition of the rely on the identification automatically obtained result instructional designers both using Elkar-DOM [15], a conc supervise the Domain Module presents the experiment co subsystem responsible for the t for extracting LOs from the elec…”

Section: Semi Automatic Construction O Modulementioning

confidence: 99%

See 1 more Smart Citation

Automating the authoring of learning material in Computer Engineering education

Conde

Larrañaga

Calvo

et al. 2012

2012 Frontiers in Education Conference Proceedings

Self Cite

View full text Add to dashboard Cite

Due to the impact that Information and Communication Technologies have in the current society in general, and in academic institutions in particular, there is a growing need for effective creation and management of digital content. But content authoring is known to be an effort and time consuming task. Digital contents used inside Technology Supported Learning Systems with learning purposes are often referred to as Didactic Resources, or Learning Objects if they are annotated with metadata. One of the main advantages of Learning Objects is that they can be reused to support learning in different platforms or environments. Along this paper an experiment carried out with ErauzOnt, a system that automatically builds Learning Objects from electronic textbooks using Natural Language Processing techniques, ontologies and heuristic reasoning is presented. ErauzOnt was able to gather definitions, examples and exercises for the topics of the Object Oriented Programming subject -a compulsory subject studied at the first year of the Computer Engineering degree at the University of the Basque Countryusing as a starting point a textbook written in English.

show abstract

Section: Semi Automatic Construction O Modulementioning

confidence: 99%

“…Along this paper an experiment carried out with ErauzOnt [5], a system that automatically builds LOs from electronic textbooks using Natural Language Processing (NLP) techniques, ontologies and heuristic reasoning, is presented. ErauzOnt was developed with the aim of being domainindependent and easily extendable to support new languages.…”

Section: Introductionmentioning

confidence: 99%

Automating the authoring of learning material in Computer Engineering education

Conde

Larrañaga

Calvo

et al. 2012

2012 Frontiers in Education Conference Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…5 One approach emphasizes the semiautomatic generation of LOs from electronic documents. For instance, [2] generate LOs from the internal representation of electronic textbooks. These systems are based on the idea of content reuse.…”

Section: Introductionmentioning

confidence: 99%

Semantic Similarity Measures for the Generation of Science Tests in Basque

Aldabe

Maritxalar

2014

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

The work we present in this paper aims to help teachers create multiple-choice science tests. We focus on a scientific vocabulary-learning scenario taking place in a Basque-language educational environment. In this particular scenario, we explore the option of automatically generating Multiple-Choice Questions (MCQ) by means of Natural Language Processing (NLP) techniques and the use of corpora. More specifically, human experts select scientific articles and identify the target terms (i.e., words). These terms are part of the vocabulary studied in the school curriculum for 13-14-year-olds and form the starting point for our system to generate MCQs. We automatically generate distractors that are similar in meaning to the target term. To this end, the system applies semantic similarity measures making use of a variety of corpus-based and graph-based approaches. The paper presents a qualitative and a quantitative analysis of the generated tests to measure the quality of the proposed methods. The qualitative analysis is based on expert opinion, whereas the quantitative analysis is based on the MCQ test responses from students in secondary school. Nine hundred and fifty one students from 18 schools took part in the experiments. The results show that our system could help experts in the generation of MCQ.

show abstract