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Mobile learning (m-learning) integrates the current mobile computing technology with educational aspects to enhance the effectiveness of the traditional learning process. This paper describes IKASYS, an m-learning management tool that provides support for the whole cycle of memorization and training activities in a wide range of domains. The tool h...
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... successful authentication, the program will dispatch exercises to the learner. In Figure 2 the application shows a generative exercise, concretely, a product between two numbers. The values of the variables are calculated in execution time taking into account that those values must satisfy exercise specifications. ...
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... Ikastolen Elkartea has wide experience in the development of didactic resources, and in recent years it has been introducing ICT into classrooms. For instance, the Confederation developed the Ikasys 9 project ( [19], [20]) to produce resources for primary and secondary school. It also led the Ostadar project, 10 a project to develop the curriculum for secondary school in the Basque Country. ...
... If we look at each text, Combination2 obtains the best average results in the Bats text; these are statistically significant. 19 It also obtains the best results in the Arctic text, although they are not statistically significant. In the Continents text, LSA3 attains slightly better difficulty values (which are not statistically significant) compared to Combination1 and Combina-tion2. ...
... Due to the limited number of students available, we had to restrict the quantitative analysis to three of the revised 19. p-value <¼ 0.05 based on one-way ANOVA. ...
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.
