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Cognitive resources allocation in computer-mediated dictionary assisted learning: From word meaning to inferential comprehension
ARTICLE

, , Department of Educational Psychology and Counseling, Taiwan ; , Department of Psychology, Education and Child Studies, Netherlands

Computers & Education Volume 127, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

Computer-mediated dictionaries have been important and widely used aids in the comprehension of, and learning from online texts. However, despite the convenience of computer-mediated dictionaries in retrieving word meaning, its use may reduce the time that readers spend reading each word and negatively affect word retention. In addition, readers' vocabulary size is a key factor influencing the lookup process, and its effectiveness. Therefore, in this study, we propose a new ‘checking-meaning’ function to optimize word retention and to explain readers' cognitive resources allocation in computer-mediated dictionary assisted learning. We conducted a 2 (checking meaning function: with vs. without) × 2 (vocabulary size: large vs. small) between-subjects design to explore the effectiveness of vocabulary acquisition and reading comprehension performance in computer-mediated dictionary-assisted reading. In line with the hypotheses, results revealed that the computer-mediated dictionary with checking-meaning function enhanced small vocabulary size learners' vocabulary acquisition, but negatively influenced large vocabulary size learners' reading comprehension performance. Based on these results, we propose the competition-cooperation relationship to explain readers' cognitive resources allocation in computer-mediated dictionary assisted learning.

Citation

Chang, Y.H., Liu, T.C. & Paas, F. (2018). Cognitive resources allocation in computer-mediated dictionary assisted learning: From word meaning to inferential comprehension. Computers & Education, 127(1), 113-129. Elsevier Ltd. Retrieved October 23, 2019 from .

This record was imported from Computers & Education on January 29, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2018.08.013

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