Kyushu University Academic Staff Educational and Research Activities Database
List of Papers
Shin'ichi Konomi Last modified date:2019.06.27

Professor / Division for Theoretical Natural Science / Faculty of Arts and Science

1. Atsushi Shimada Shinichi Konomi Hiroaki Ogata, Real-time learning analytics system for improvement of on-site lectures, Interactive Technology and Smart Education, 10.1108/ITSE-05-2018-0026, 15, 4, 314-331, 2018.11, Purpose: The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc. Design/methodology/approach: The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation. Findings: Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences. Originality/value: Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately..
2. Huiyong Li Brendan Flanagan Shin'ichi Konomi Hiroaki Ogata, Measuring Behaviors and Identifying Indicators of Self-Regulation in Computer-Assisted Language Learning Courses, Research and Practice in Technology Enhanced Learning, 10.1186/s41039-018-0087-7, 13, 1, 2018.12, The aim of this research is to measure self-regulated behavior and identify significant behavioral indicators in computer-assisted language learning courses. The behavioral measures were based on log data from 2454 freshman university students from Art and Science departments for 1 year. These measures reflected the degree of self-regulation, including anti-procrastination, irregularity of study interval, and pacing. Clustering analysis was conducted to identify typical patterns of learning pace, and hierarchical regression analysis was performed to examine significant behavioral indicators in the online course. The results of learning pace clustering analysis revealed that the final course point average in different clusters increased with the number of completed quizzes, and students who had procrastination behavior were more likely to achieve lower final course points. Furthermore, the number of completed quizzes and study interval irregularity were strong predictors of course performance in the regression model. It clearly indicated the importance of self-regulation skill, in particular completion of assigned tasks and regular learning..
3. Shin'ichi Konomi Tomoyo Sasao Simo Hosio Kaoru Sezaki, Using Ambient WiFi Signals to Find Occupied and Vacant Houses in Local Communities, Journal of Ambient Intelligence and Humanized Computing, Springer.,, 10, 2, 779-789, 2018.12, In many countries, the population is either declining or rapidly concentrating in big cities, which causes problems in the form of vacant houses. It is often challenging to keep track of the locations and the conditions of vacant houses, and for example in Japan, costly manual field studies are employed to map the occupancy situation. In this paper, we discuss a technique to infer the locations of occupied and vacant houses based on ambient WiFi signals. Our technique collects Received Signal Strength Indicator (RSSI) data based on opportunistic smartphone sensing, constructs hybrid networks of WiFi access points, and analyzes their geospatial patterns based on statistical shape modeling. In situ experiments in two residential neighborhoods show that the proposed technique can successfully detect occupied houses and substantially outperform a simple triangulation-based method in one of the neighborhoods. We also argue that the proposed technique can significantly reduce the cost of field surveys to find vacant houses as the number of potential houses to be inspected decreases..
4. Tomoyo Sasao Shinichi Konomi Vassilis Kostakos Keisuke Kuribayashi Jorge Goncalves, Community Reminder
Participatory contextual reminder environments for local communities, International Journal of Human Computer Studies, 10.1016/j.ijhcs.2016.09.001, 102, 41-53, 2017.06, Many projects have looked at how communities can co-design shared online repositories, such as Wikimapia and Wikipedia. However, little work has examined how local communities can give advice and support to their members by creating context-aware reminders that may include advice, tips and small requests. We developed the Community Reminder environment, a smartphone-based platform that supports community members to design and use context-aware reminders. We have conducted a one-month field study of Community Reminder to crowdsource and deliver safety-relevant information in a local community. The results show the benefits of involving community members in reminder design and connecting different perspectives. We also show that the proposed approach can broaden participation in local communities..
5. Simo Hosio Jorge Goncalves Niels van Berkel Simon Klakegg Shin'ichi Konomi Vassilis Kostakos, Facilitating Collocated Crowdsourcing on Situated Displays, Human-Computer Interaction, 10.1080/07370024.2017.1344126, 33, 5-6, 335-371, 2017.08, Online crowdsourcing enables the distribution of work to a global labor force as small and often repetitive tasks. Recently, situated crowdsourcing has emerged as a complementary enabler to elicit labor in specific locations and from specific crowds. Teamwork in online crowdsourcing has been recently shown to increase the quality of output, but teamwork in situated crowdsourcing remains unexplored. We set out to fill this gap. We present a generic crowdsourcing platform that supports situated teamwork and provide experiences from a laboratory study that focused on comparing traditional online crowdsourcing to situated team-based crowdsourcing. We built a crowdsourcing desk that hosts three networked terminal displays. The displays run our custom team-driven crowdsourcing platform that was used to investigate collocated crowdsourcing in small teams. In addition to analyzing quantitative data, we provide findings based on questionnaires, interviews, and observations. We highlight 1) emerging differences between traditional and collocated crowdsourcing, 2) the collaboration strategies that teams exhibited in collocated crowdsourcing, and 3) that a priori team familiarity does not significantly affect collocated interaction in crowdsourcing. The approach we introduce is a novel multi-display crowdsourcing setup that supports collocated labor teams and along with the reported study makes specific contributions to situated crowdsourcing research..
6. Muneeba Raja Anja Exler Samuli Hemminki Shin'ichi Konomi Stephan Sigg Sozo Inoue, Towards pervasive geospatial affect perception, GeoInformatica, 10.1007/s10707-017-0294-1, 22, 1, 143-169, 2018.01, Due to the enormous penetration of connected computing devices with diverse sensing and localization capabilities, a good fraction of an individual’s activities, locations, and social connections can be sensed and spatially pinpointed. We see significant potential to advance the field of personal activity sensing and tracking beyond its current state of simple activities, at the same time linking activities geospatially. We investigate the detection of sentiment from environmental, on-body and smartphone sensors and propose an affect map as an interface to accumulate and interpret data about emotion and mood from diverse set of sensing sources. In this paper, we first survey existing work on affect sensing and geospatial systems, before presenting a taxonomy of large-scale affect sensing. We discuss model relationships among human emotions and geo-spaces using networks, apply clustering algorithms to the networks and visualize clusters on a map considering space, time and mobility. For the recognition of emotion and mood, we report from two studies exploiting environmental and on-body sensors. Thereafter, we propose a framework for large-scale affect sensing and discuss challenges and open issues for future work..