In Japan, a tornado is an infrequent natural disaster; however, tremendous winds can cause considerable damage. To prepare for such events, simulated tornado experience (STE) considering tremendous wind as a major hazard and incorporating evacuation plans must be provided. However, introduction of a large wind generator in STE is difficult. We developed simple VR and AR systems (VR-STE and AR-STE) that render STE by realistically simulating tornadoes and conducted comparative experiments. We set research questions that aimed to clarify how VR- and AR-STEs differ from STE, which expresses tornadoes with audiovisual effects and real, tremendous wind generated by a large wind generator. The survey variables were fear, self-efficacy, and learning motivation to cope with a tornado. The experimental results revealed that audiovisual effects can retain learners’ fear of a tornado and influence learners’ self-efficacy and learning motivation. Furthermore, VR-STE is more suitable than AR-STE in terms of controlling fear and system operation.
In the past decade, Japan’s nurses have benefitted from an increase in distance learning opportunities. However, there is little information on course completion and successful learning outcomes, making it difficult to implement appropriate measures that support distance learning, such as orientation courses and mentoring from faculties. This study applies learning analytics to distance learning logs and learner information to build a learning support program suitable for learners in the field of nursing. Our findings show that login frequency regarding a distance-learning course for nurses was related to course completion, as was login frequency to an orientation course three months after the start of the program. These results have implications for how educators monitor learning status and implement support. The findings, and their implications for instructional design and educator effectiveness, are applicable to all health professionals who receive education and training through distance learning.
In the study of intelligent tutoring systems (ITSs), research on learner modeling has been actively conducted to provide effective support adapted to a learner's state of understanding, in which learning history, learning style, lack of knowledge, misunderstandings (bugs), etc. have been discussed. On the other hand, low-performance learners who genuinely need assistance find themselves in trouble because they do not know what to do, that is, they are in a state of “not understanding.” As far as the author knows, however, there is no research on modeling the state of “not understanding.” In this study, we propose a new methodology of modeling the state of “not understanding” of a learner directly based on the state space and search model used in the General Problem Solver (GPS) in AI. Then, we develop a theory for generating feedback that adequately supports low-performance learners, which can be justified by the theory of the proposed learner model.
This paper attempts to discover the distinguishing characteristics of Japanese national universities, and to determine the strengths of each university by analyzing their midterm goals. We propose a method that extracts words from documents, analyzes them, and visualizes the features of documents on the basis of these extracted words. We developed two systems, a cross-tabulation search engine and a keyword map search engine, for the midterm goal document. In these systems, the user both decides the viewpoint to be compared and analyzed and inputs the search word, so that the relation of the words related to the search word is visualized, and a novel comparison analysis method among university organizations is realized. In this paper, we report the outline of these systems and the results of the verification of the two systems, using “regional contribution” as an example.
Instructional design significantly impacts participants’ learning experiences and retention in massive open online courses (MOOCs). Although MOOCs have been increasingly adopted on a wide scale, structure of concrete design components for ensuring high learning experience quality have not been elucidated so far. This study conducted detailed examination of five existing MOOCs from multiple countries using revised Bloom’s taxonomy and Merrill’s component display theory. The cognitive levels determined by the learning contents and assessment activities were analyzed and correlated with instructional sequential analysis. The results provided clear distinctions for good practice, thereby indicating prescriptive design choices to realize learning objectives for diverse learners.