文章基本信息

标题：Bayesian Approach to Analyze Reading Comprehension: A Case Study in Elementary School Children in Mexico
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作者：Ernesto U. Rodriguez-Barrios ; Roberto Angel Melendez-Armenta ; Sandra G. Garcia-Aburto 等
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2021
卷号：13
期号：8
页码：4285
DOI：10.3390/su13084285
语种：English
出版社：MDPI, Open Access Journal
摘要：In the educational field, reading comprehension is connected to learning achievement, and through it, one can interpret, retain, organize and value what has been read. It is an essential ability for the understanding and processing of information in learning. Furthermore, it is an essential skill to developing sustainable education. In this sense, sustainable development needs an advanced reading comprehension ability at elementary school in order to teach and learn future knowledge areas such as climate change, disaster risk reduction, biodiversity, poverty reduction, and sustainable consumption. Nevertheless, there have been few works focused on analyzing reading comprehension, particularly in Mexico, where the reference is the Programme for International Student Assessment (PISA) test on how well the Mexican students have developed this skill. Hence, this article shows the usefulness of employing Bayesian techniques in the analysis of reading comprehension at elementary school. The Bayesian network model allows for the determination of the language and communication level of achievement based on parameters such as learning style, learning pace, speed, and reading comprehension, obtaining an 85.36% precision. Moreover, the results confirm that teachers could determine changes in lesson planning and implement new pedagogical mechanisms to improve the level of learning and understanding contents.
关键词：Bayesian networks; reading comprehension; education; artificial intelligence