The aim of this study is to validate MOOC-based curriculum in higher education in Iran. The research method is mixed and of exploratory type. Method of grounded theory is used in the qualitative section and the Validation Test Pattern among the faculties and PhD students of Tehran's Universities is considered in the quantitative part. The statistical population of the qualitative section include 14 experts in educational technology and the quantitative section include 214 members of the faculties and PhD students of Tehran's Universities who were chosen by Stratified random sampling. To collect data, we used semi-structured interview in the qualitative section and scholar's questionnaire in the quantitative part. Results show that there are 28 general issues in paradigmatic model is obtained which included Terms of cause, Central phenomenon, Underlying conditions, Confounding conditions, Strategies and Implications that reflected effective factor in MOOC-based Curricula in higher education.
This paper presents an artificial neural network based solution method for modelling the pitting resistance of AISI 316L stainless steel in various surface treated forms. Surface treatment is a promising technique for improving the corrosion resistance of stainless steels. In this study, cyclic polarisation tests were performed before and after surface treatment. Experimental results were modelled by the neural network. The artificial neural network model exhibited superior performance based on the fitness of the observed versus predicted data. The results showed that the predicted data from the neural network model were considerably similar to the experimental data. The model has been saved and can easily be used to predict the corrosion in different surface treatment methods.
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