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.

The management of a fully online course is a key to maintaining learning effectiveness. Some factors of participants and their learning activities were analysed. Features of students who preferred joining fully online courses were extracted and compared across several years. Note taking activity during the course was lexically analysed and the features of notes were extracted. The relationships between these features and learning performance was compared. Five typical note taking styles were extracted, and reflections of learning performance and participants' characteristics were examined.

Lexical feature patterns of notes students took during a blended learning course were classified and the student's transitions between clusters were analysed using a formative evaluation. The lexical features were extracted from relationships between the contents of notes students took and the contents presented by the lecturer. Five clusters were extracted using four dimensional features and cluster analysis, and most participant's note taking patterns in the first half of the course transformed from one of two clusters to one of five clusters, which included three additional new clusters, in the second half. This was due to note taking skills instruction given by the lecturer. The contributions of participants' improved note taking skills and their reflections were evaluated quantitatively, and the mean features of notes taken and mean learning performance between clusters were also compared.

Participant's emotional factors for a fully online course were measured twice during course using some metrics which were used for a blended learning course. Self efficacy and reflection during learning courses with instruction of note-taking skills were analysed. The contribution of courses and survey points were not significant for gathered responses from participants except "learning hours". These responses of participant's reflection were affected by their characteristics and self efficacy. The relationships across survey metrics were compared across two courses using a causal analysis technique. Also, changes of metrics during the course positively affected to participant's reflections.

Audio information plays a key role in the learning environment. The effectiveness of audio information on note-taking and learning performance was examined using survey data from a fully online course. First, metrics of note-taking activities were measured using the relationships between these lexical features, and their contributions to learning performance were then examined. In particular, the effectiveness of learning materials presented as slide contents or audio information was compared. Second, some relationships between these metrics and the scores of learning performance evaluations were analysed, and the effectiveness of audio information was qualified using regression analysis. The results indicate that audio information significantly affects both the contents of notes taken and learning performance.

To assess the transformation of a lecturer's term-based concepts presented to students, both the lecturer's presentation and the student's notes in a blended learning course were lexically analyzed and represented in graphs. The contents of these notes were mathematically compared and evaluated. Graphs were then created using the noun co-occurrences in the notes, and the differences in distances between the graph of the lecturer's presentation and the graph of the student's notes were calculated. In the results, students recorded additional nouns to replace the nouns given by the lecturer, and introduced new nouns of their own as substitutes as well. Also, the terms students wrote in their notes were not recorded separately, but were linked with other terms.

The lexical features of notes taken during blended and fully online courses were analysed to improve learning activity, as the contents of notes can be used to facilitate course evaluation. The lexical contents of student's notes was used to analyse the note taking behaviour in both blended and fully online courses. Features such as the number of terms, the word ratios of student's notes and the degree of coverage of the lecturer's notes were compared. The relationships between learning performance and note-taking behaviour were also analysed. The causal relationships between student's characteristics and note assessments were statistically compared, and contributing factors were extracted.

In order to visualize and analyze the learning process of students who participated in a fully online engineering course, note-taking activity was used and lexical analysis of notes taken by students was made. The features of notes taken were compared with the contents of the lecturer's presentation slides and oral instructions. Also, the latent semantic indexing technique was applied to the student's notes and the lecturer's presentations, and the lexical transformation of the course content was analyzed.

A fully online learning environment requires support procedures in order to promote pro-active learning. This paper presents the causal relationships between student's characteristics, note-taking skills, learning experience, note assessment and test scores. A structural equation modeling technique is used to trace student's learning activity as note-taking occurs. The results of this modeling technique suggest that key factors and their contributions to course performance can be measured. Also, the factors which contribute to note-taking activity were examined.