Automatic Evaluation Methods of Trainee's Answers to Develop a 4R Risk Prediction Training System

Hirotsugu Minowa, Hiromi Fujimoto, Koichi Takeuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

4 Rounds (4R) training method is practiced in industrial office site for reducing accidents caused by human factors. The 4R method enables to raise hazard-prediction capability of worker such as coping, decision-making to avoid danger situation. The workers as trainees train on their own by finding hazards which lurked in the hazard prediction training (KYT in Japanese) sheet. However, there is a large problem that a single trainee cannot train oneself using 4R method because the training of 4R method needs instruction of expert as human instructor. To solve that problem, we aim to develop hazard prediction training system. The advantage of this system enables trainee to train oneself anytime/anywhere using 4R method. In this research paper, we reports about our proposal of training system, the development of subsystem which based on machine learning to evaluate trainee's answer correct or not, and reports the result of evaluation experimental that showed the average accuracy was 63.0±22.9 [%].

Original languageEnglish
Title of host publicationProceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-286
Number of pages4
ISBN (Print)9781479999583
DOIs
Publication statusPublished - Jan 6 2016
Event4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015 - Okayama, Japan
Duration: Jul 12 2015Jul 16 2015

Other

Other4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015
CountryJapan
CityOkayama
Period7/12/157/16/15

Fingerprint

Hazards
Human engineering
Learning systems
Accidents
Decision making

Keywords

  • e-learning
  • machine learning
  • natural language processing
  • training

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Minowa, H., Fujimoto, H., & Takeuchi, K. (2016). Automatic Evaluation Methods of Trainee's Answers to Develop a 4R Risk Prediction Training System. In Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015 (pp. 283-286). [7373916] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2015.301

Automatic Evaluation Methods of Trainee's Answers to Develop a 4R Risk Prediction Training System. / Minowa, Hirotsugu; Fujimoto, Hiromi; Takeuchi, Koichi.

Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 283-286 7373916.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Minowa, H, Fujimoto, H & Takeuchi, K 2016, Automatic Evaluation Methods of Trainee's Answers to Develop a 4R Risk Prediction Training System. in Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015., 7373916, Institute of Electrical and Electronics Engineers Inc., pp. 283-286, 4th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2015, Okayama, Japan, 7/12/15. https://doi.org/10.1109/IIAI-AAI.2015.301
Minowa H, Fujimoto H, Takeuchi K. Automatic Evaluation Methods of Trainee's Answers to Develop a 4R Risk Prediction Training System. In Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 283-286. 7373916 https://doi.org/10.1109/IIAI-AAI.2015.301
Minowa, Hirotsugu ; Fujimoto, Hiromi ; Takeuchi, Koichi. / Automatic Evaluation Methods of Trainee's Answers to Develop a 4R Risk Prediction Training System. Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 283-286
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