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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2018 Vol.8(1): 38-45 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.1.1009

Estimating Work Situations from Videos of Practical Training Classes with Assembly Tasks

K. Okamoto, K. Kakusho, M. Yamamoto, T. Kojima, and M. Murakami
Abstract—Our preceding study proposed the possibility of producing video previews that enhance viewer motivations towards assembly work in practical training classes by picking scenes of work situations with good performance from the videos of past classes. In the study, two conceptual attributes for categorizing work situations from the viewpoint of the performance were introduced with reference to previous studies for evaluating productivity of human intellectual work with computers. Based on those two conceptual attributes, our preceding study employed observable features for estimating work situations in the videos and showed that those features seem to reflect the difference of work situations with respect to the conceptual attributes. However, quantitative precision for estimating work situations from those features has not yet been evaluated. Moreover, those observable features are employed without considering whether humans actually pay attention to them. It is also not clear whether videos with work situations sufficient for each of the conceptual attributes actually enhance viewer motivations towards the work. This article clarifies these issues based on our recent experimental results with experimental participants.

Index Terms—Practical training class, assembly task, work situation estimation, observable feature, physical activity, mental concentration.

Kai Okamoto, Koh Kakusho, and Michiya Yamamoto are with School of Science and Technology, Kwansei Gakuin University, Sanda, Japan (e-mail: {kaisea, kakusho, michiya.yamamoto}@kwansei.ac.jp).
Takatsugu Kojima is with Faculty of Medicine, Shiga University of Medical Science, Otsu, Japan (e-mail: kojima@kojima-lab.net).
Masayuki Murakami is with Research Center for Multi-Media Education, Kyoto University of Foreign Studies, Kyoto, Japan (e-mail: masayuki@murakami-lab.org).

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Cite: K. Okamoto, K. Kakusho, M. Yamamoto, T. Kojima, and M. Murakami, "Estimating Work Situations from Videos of Practical Training Classes with Assembly Tasks," International Journal of Information and Education Technology vol. 8, no. 1, pp. 38-45, 2018.

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