田村 恭久

<p>This paper focused on students' peer assessment and analyzed evaluation behavior using log data from the evaluation process. Peer assessments by students are problematic in terms of reliability and validity. Many previous studies have discussed the reliability and validity of peer assessments using the evaluation scores of peer assessments. In contrast, the evaluation process, that is, who, when, and which items were evaluated, and in what order, has not been studied. The purpose of this research is to answer this research question, "How do students evaluate each other during peer assessments?" To achieve this purpose, we break the research question into three sub-questions: (1) Is it worth focusing on evaluation behaviors? (2) How can we visualize evaluation behaviors? and (3) What are the characteristic evaluation behaviors? First, we investigated similar studies and considered their relationship with our proposal. Consequently, it became clear that in social science, there have been many studies that acquire process data for answering surveys and questionnaires. Therefore, also in peer assessment research, there is a possibility to gain a better understanding of how students participate in peer assessment by focusing on the process log. Second, we developed a web-based form as the peer assessment tool to detect students' evaluation process data. Using this form, we conducted an experiment to acquire evaluation process data from a class in which assessments were made to visualize evaluation behavior. Finally, we extracted and discussed characteristic evaluation behaviors. Consequently, we identif ied characteristic evaluation behaviors such as "evaluated in a short time." We expect these results will lead to a clarif ication of the causes of low reliability and validity in students' peer assessments that have been dif f icult to assess through conventional methods based only on the peer assessment score. Specif ically, the causes are, for example, "the students were not evaluating seriously," "they evaluated seriously but could not evaluate properly because of a lack of evaluation ability," or "there was room for improvement in the form design or lesson conditions."</p>

The authors verified learners' learning styles with use of learning style questionnaires and page flip history of materials. First, page-flip detection function of material slides on learners' PCs was developed. This function also transfers the result to a dedicated LRS (Learning Record Store) server automatically. From this fine-grained learning activity log, student's learning styles were classified. Second, the authors estimated learners' learning styles with use of widely used learning style questionnaires. The authors verified these two types of learning styles for 100 subjects (research participant) in real 4 units of 2 classes. As a result, a student who "rushes up ladder" during a certain time period was significantly relevant to "Global" learning style tendency rather than "Sequential" one.

This paper introduces an automatic analysis and feedback system for amateur baseball pitching learners with use of Microsoft Kinect. Compared with conventional 3-D motion capture systems, Kinect has advantages of reasonable cost and easiness of application system development for physical exercises. The authors developed a similar system for flying disc throw, which is disclosed in Tamura, Yamaoka et al. (2013) and Tamura, Uehara et al. (2013). In this paper a target motion is moved to baseball pitching, so focused body parts and judgment criteria were changed, although utilizing the same Kinect platform. The proposing system acquires postures and motions of amateur baseball pitchers, and judges them in 2 criteria: (1) maximum angle of elbow, and (2) hand twist, which were decided by a training expert. It also displays feedback messages to improve their motions. As a result, novice testees of the target group showed significant improvement of their pitching motions.

This paper shows typical functions required on electronic textbooks to be adopted in various countries, trial implementation of these functions, and their verification of representation and behavior in six e-book platforms. First, the authors extracted the functions by checking preceding researches and investigations. The result contains 52 items, including 7 categories of “authentication”, “copyright”, “contents representation”, “related information”, “additional information by learners”, “learning support”, and “restriction of contents and platforms”. Next, these functions were implemented with ePub3 format e-books. ePub3 is going to be one of de jure standard format of e-books. Also, contents representation and behavior of the result ePub e-books were verified on 6 types of e-book platforms on operating systems of iOS, Android, Windows, and Kindle Linux. The verification result shows that iBooks3 on iOS worked best among the 6 e-book readers. Also it shows that many e-book readers support only representation of ePub2 files, while HTML5, JavaScript, and Web API are not supported yet.