Evaluating an automatic adaptive delivery method of English words learning contents for university students in science and technology

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Abstract

Today's e-Learning, blending portable digital devices and common PC devices under well-maintained Internet infrastructure, provides convenient learning environment where everyone can learn when they need. In particular, self-study with simple multiple-choice questions with LMS (Learning Management System) is available with no need to score by lecturer because usual LMS implements automatic scoring capability. Since LMS is useful for both lecturers and students, in Japan it has been already accepted as being indispensable in educational institutions around universities. However, this type of learning can only support just gathering of knowledge, but not assist the improvement of the skill to leverage knowledge. Currently it has not been enough to provide e-Learning with capabilities of both knowledge acquisition and utilization, so to realize it the authors started the development of user adaptive learning service which deals with English learning. This paper focuses on the automatic delivery of learning contents for each user's mobile device, and verifies practical effectiveness of item response theory for user adaptive learning contents provision. This paper develops a server program to automatically deliver learning materials consisted of some multiple choice questions. Based on the result of preliminary experiment, for realizing e-Learning service with learner adaptation functionality, the conceptions to evaluate each user's learning effort and to realize the learning of knowledge acquisition are discussed. © 2014 Springer International Publishing.

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APA

Matsumoto, S., Kurisu, T., Kashima, T., & Akiyoshi, M. (2014). Evaluating an automatic adaptive delivery method of English words learning contents for university students in science and technology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8510 LNCS, pp. 510–520). Springer Verlag. https://doi.org/10.1007/978-3-319-07233-3_47

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