Managing Human Factors Related Risks. The Advanced Training Model in Dangerous Goods Transport on Roads
DOI:
https://doi.org/10.3991/ijep.v8i4.8150Keywords:
DG training courses, teaching methods, qualitative comparison analysis, blended learningAbstract
This paper studies the methodological essence of dangerous goods (DG) training courses for drivers and dangerous goods safety advisers (DGSA). The aim of the research is to advance existing teacher-centered course model in Estonia with learner-centered methods that best suit specific objectives and meet expected learning outcomes, as well as to improve DG training model with the integrated use of interactive teaching methods. The paper presents a qualitative development research strategy based on studies regarding ADR regulations training courses in Estonia as well as on the analysis of teaching methods applied in the professional training of adults. The data is collected in two steps: firstly by implementing questionnaires for consignors/ consignees, freight forwarders carrier companies and drivers, secondly during in-depth interviews/ focus group meeting with DG regulations training companies’ providers. Implementing methodology of qualitative comparison analysis (QCA) combination of best suitable teaching methods is identified. After following in-depth interviews and performing a focus group, these combinations are further used as input for developing existing course model with integrated use of blended learning alternatives, where digital media meets with traditional classroom meth-ods. Results of this research contribute coming up with interactive methodological approach within ADR regulations training courses that meet the best trainees’ expectations and fulfills the risk management aim.
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Published
2018-06-20
How to Cite
Janno, J., & Koppel, O. (2018). Managing Human Factors Related Risks. The Advanced Training Model in Dangerous Goods Transport on Roads. International Journal of Engineering Pedagogy (iJEP), 8(4), pp. 70–88. https://doi.org/10.3991/ijep.v8i4.8150
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