Title of article :
Investigation and Selection of the Most Efficient Method of Citizenship Education for Household Waste Source Separation Based on the KHANFAHP Model
Author/Authors :
Farajollahi ، Mehran Payam-e-Noor University , Sarmadi ، Mohammad Reza Payam-e-Noor University , Abbasi ، Asadollah Payam-e-Noor University , Maleki ، Hamid Payam-e-Noor University , Azizi ، Mohammad Payam-e-Noor University
Pages :
15
From page :
161
To page :
175
Abstract :
The learning system provided by the municipalities is one of the most important motivating factors make citizens to participate in urban management plans such as source separation of wastes. In the past years, Tehran municipality has been focusing on providing different training in waste management and specifically source separation, which has not been able to attract public participation. The aim of this paper is to study the failure causes of learning systems and to address the shortcomings in the form of distance learning to provide the most effective learning method. In this regard, the Khan conceptual model has been used in the fuzzy analytic hierarchy process. Finally, the results of the 100 paired comparison questionnaires in this study, which were obtained using Khan’s conceptual model and fuzzy theory, introduced mobile applications as the best method of distance learning. The strengths and reasons for its selection, which were obtained from interviews conducted by residents of district 16 of Tehran municipality, are described. To compare between different types of distance learning methods, 8 criteria were used in 25 proposed subcategories categorized by Khan’s conceptual model that could integrate the concepts and basis of a standardized and successful distance education system into a fuzzy decision model.
Keywords :
District 16 of Tehran municipality , Tehran , Khan Model , Source separation , Analytical hierarchy analysis
Journal title :
Environmental Energy and Economic Research (EEER)
Serial Year :
2018
Journal title :
Environmental Energy and Economic Research (EEER)
Record number :
2448887
Link To Document :
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