ERIC Number: EJ1189141
Record Type: Journal
Publication Date: 2018-Sep
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1360-2357
EISSN: N/A
Neural Network Approach to Predict Mobile Learning Acceptance
Al-Shihi, Hafedh; Sharma, Sujeet Kumar; Sarrab, Mohamed
Education and Information Technologies, v23 n5 p1805-1824 Sep 2018
The proliferation of mobile computing technologies is playing major role in the growth of mobile learning (M-learning) market around the globe. The purpose of this paper is to develop a research model in the lines of commonly used models the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM) by incorporating constructs namely flexibility learning, social learning, efficiency learning, enjoyment learning, suitability learning, and economic learning that can predict M-learning adoption in a developing country. The data were collected from 388 students from all major universities/colleges in the capital city (Muscat) of Oman. The neural network modeling was employed to predict M-learning adoption. The neural network modeling results showed that flexibility learning, social learning, efficiency learning, enjoyment learning, suitability learning, and economic learning variables have significant influence on the intention of students to accept mobile learning. The key outcomes of this study suggest important determinants that can assist academic administrators and telecommunication service providers to enhance the adoption of M-learning with the help of suitable strategy.
Descriptors: Predictor Variables, Technology Uses in Education, Models, College Students, Foreign Countries, Electronic Learning, Educational Technology
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Oman
Grant or Contract Numbers: N/A