The possibility of predicting individual scores of critical thinking ability from estimated ratings of essay texts was examined using machine learning techniques. First, the feasibility of predicting critical thinking ability scores from scores of personality and literacy of science and technology surveys was confirmed. Scores for personal characteristics were estimated using automated assessments which were based on a pre-trained language processing model of two types of essay texts. Second, variables employed in the surveys used to predict scores of critical thinking ability were replaced step by step with estimated values from the essay texts. Prediction performance decreased gradually, and the accuracy with all estimated values was above the level of performance which was predicted using the essay text analysis. As a result, the possibility of predicting scores from essay text assessments was examined.

The possibility of predicting anticipated scores representing student's characteristics from their essay texts using a machine learning technique was examined. Student's characteristics include critical thinking disposition ability, disaster-prevention conscientiousness, and personality. First, the potential for predicting essay assessment scores from essay texts using machine learning with each set of essay reports or essay comments was confirmed. However, prediction performance of one other set of essays and other scores of student's characteristics was insufficient. When the training procedure was re-designed using both sets of essays, performance for some scores improved. The limited performance was examined and further improvement procedure was discussed.

Sentences in student's essay reports were analysed to examine the feasibility of evaluating ability of critical thinking disposition in order to develop this ability during a fully online course in a university class. Features of essay reports, dependency of terms, and the representation of selected terms between levels of critical thinking disposition which were measured using a questionnaire were compared. The results show that the frequency of dependencies reflects the level of factor scores of critical thinking disposition and that multi-dimensional scales of these frequencies can illustrate the relationships between dependencies and levels of ability. Also, senses which contained affirmative or negative contents are evaluated using a corpus of affirmative and negative words used in essay reports. These frequencies are influenced by the level of ability as well.

The progress of development of the disposition toward critical thinking in blended and fully online learning environments is compared. Participant’s individual characteristics, including responses to a questionnaire about critical thinking disposition, were measured during a bachelor level university course. Though the measured scores are comparable, the relationships between the scores in the two surveys during the course are different according to learning style. The contributing variables were extracted using linear regression analysis, and the weights assigned to them depended on the style of learning. In particular, variables which contributed to transforming the participant’s ability are discussed. Also, cluster patterns of scores of literacy of science and technology were extracted, and the contributions of the cluster scores was discussed.