A Study of E-Learning Maturity in Higher Agricultural Education Using Artificial Neural Network

Abstract

Background: Being a relatively new learning mode, e-learning offers a comprehensive solution for those institutions seeking to adopt modern technologies and transform their teaching methods and environments. The present study employed Misra and Dhingra model to assess e-learning maturity in the field of agriculture in Iran using artificial neural network. Methods: This cross-sectional study was performed based on six levels of Misra and Dhingra model. It was conducted among 340 graduate agriculture students and 177 faculty members at four universities including Bu Ali Sina, Tehran, Hormozgan, and Mashahd universities. The participants were selected based on proportional sampling method from July 1 to September 28, 2019. A validated researcher-made questionnaire was used for data collection, and WEKA software version 3.9.2 was employed for data analysis. Results: The results indicated that the level of academic achievement in the field of agriculture can be predicted based on the Misra and Dhingra model. Furthermore, e-learning was at the fourth level in this field in Iran’s higher education. Conclusions: In general, educational facilities and infrastructures were identified to be suitable for e-learning implementation in Iran’s higher education institutions.

Keywords

• e-learning
• electronic maturation
• weka software
• ann