Estimating Scores of Critical Thinking Ability Using Essay Text Assessments

Minoru Nakayama, Masaki Uto, Satoru Kikuchi, Hiroh Yamamoto

Abstract: 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.