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In order for society to be better prepared to respond to problems and issues involving natural disasters, it may be possible to predict critical thinking ability scores with ease using an individual’s fundamental characteristics, such as personality and literacy of science and technology. The authors have extracted some of the relationships of these surveyed metrics in previous studies. This paper examines the possibility of estimating scores of critical thinking disposition (CTD) and disaster-prevention consciousness (DPC) using individual factor scores for personality (Big-5) and literacy of science and technology (LST), and a regression prediction procedure. The possibility and validity have been confirmed using student survey data which was gathered over two years during a fully online course at a Japanese university. In particular, the correlation coefficients calculated during the second survey showed the same level of results as with the modelling data from the first survey. Therefore, the results show the possibility that the extracted functions can be applied to other sets of data. Also, learners’ final performance levels may be predictable using the initial survey data set, thus making them a valuable tool for learning support assistance.

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Predicting Critical Thinking Ability Scores Using Student’s Characteristics and Learning Performance

  • 会议论文

  • 发表日期：

    2025年

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  • Minoru Nakayama,
  • Satoru Kikuchi,
  • Masaki Uto,
  • Hiroh Yamamoto

摘要

In order for society to be better prepared to respond to problems and issues involving natural disasters, it may be possible to predict critical thinking ability scores with ease using an individual’s fundamental characteristics, such as personality and literacy of science and technology. The authors have extracted some of the relationships of these surveyed metrics in previous studies. This paper examines the possibility of estimating scores of critical thinking disposition (CTD) and disaster-prevention consciousness (DPC) using individual factor scores for personality (Big-5) and literacy of science and technology (LST), and a regression prediction procedure. The possibility and validity have been confirmed using student survey data which was gathered over two years during a fully online course at a Japanese university. In particular, the correlation coefficients calculated during the second survey showed the same level of results as with the modelling data from the first survey. Therefore, the results show the possibility that the extracted functions can be applied to other sets of data. Also, learners’ final performance levels may be predictable using the initial survey data set, thus making them a valuable tool for learning support assistance.