This paper describes the development of a software program that supports argumentative reading and writing, especially for novice students. The software helps readers create a graphic organizer from the text as a knowledge map while they are reading and use their prior knowledge to build their own opinion as new information while they think about writing their essays. Readers using this software can read a text, underline important words or sentences, pick up and dynamically cite the underlined portions of the text onto a knowledge map as quotation nodes, illustrate a knowledge map by linking the nodes, and later write their opinion as an essay while viewing the knowledge map; thus, the software bridges argumentative reading and writing. Sixty-three freshman and sophomore students with no prior argumentative reading and writing education participated in a design case study to evaluate the software in classrooms. Thirty-four students were assigned to a class in which each student developed a knowledge map after underlining and/or highlighting a text with the software, while twenty-nine students were assigned to a class in which they simply wrote their essays after underlining and/or highlighting the text without creating knowledge maps. After receiving an instruction regarding a simplified Toulmin's model followed by instructions for the software usage in argumentative reading and writing along with reading one training text, the students read the target text and developed their essays. The results revealed that students who drew a knowledge map based on the underlining and/or highlighting of the target text developed more argumentative essays than those who did not draw maps. Further analyses revealed that developing knowledge maps fostered an ability to capture the target text's argument, and linking students' ideas to the text's argument directly on the knowledge map helped students develop more constructive essays. Accordingly, we discussed additional necessary scaffolds, such as automatic argument detection and collaborative learning functions, for improving the students' use of appropriate reading and writing strategies.

In this paper we propose new quantitative metrics that express the characteristics of current general practices in slidebased presentation methodology. The proposed metrics are numerical expressions of:`To what extent are the materials being presented in the prepared order?' and`What is the degree of separation between the displays of the presenter and the audience?'. Through the use of these metrics, it becomes possible to quantitatively evaluate various extended methods designed to improve presentations. We illustrate examples of calculation and visualization for the proposed metrics.

In this paper, we propose a new method of constructing machine learning models for predicting academic success. In this method, a multi-objective genetic algorithm is deployed to select explanatory variables for the predictive model as an approach that takes into account both elements of predictive performance and interpretability. By using two evaluation functions, i.e., prediction performance and the number of explanatory variables, our method can find the Pareto-optimal solution set that reflects these trade-offs. The numerical simulation results show that our method can obtain a model set that takes into account the trade-off between the accuracy and complexity of the predictive model, although there are differences in behavior depending on the academic success indices to be predicted.

Teaching assistants (TAs) play a key role in helping undergraduate university students with their studies. However, there is a lack of formal training provided to TAs and their role is not always clearly defined. Project Associate Professor Mio Tsubakimoto, University of Tokyo, Japan, is seeking to make improvements to this situation by enhancing the education provided to TAs and, in the process, improving university education. FIrst, Tsubakimoto set out to understand the role played by TAs from the perspective of students, teachers and the TAs themselves and build a picture of the set of skills and techniques that make a good TA. To do this she qualitatively and quantitatively studied how the different classes and lectures that make up First Year Seminars (FYS) were taught, as well as surveying TAs, with a view to implementing improvements to TA training. These investigations led to the development and distribution of a guide for TA training and content that incorporates active learning. Following two years of training TAs using the guide, Tsubakimoto repeated the surveys in order to assess the ways in which the implementation of the guide had enhanced TA performance. She found that the presence of trained TAs led to improved student and faculty performance. The research underlined the benefits of the presence of trained TAs in the classroom for university learning, both for the students and for the TAs themselves, enabling them to reach their full potential.