Informatics Publishing Limited

Muasaad Alrasheedi ¹, Luiz Fernando Capretz ¹, Arif Raza ¹

Affiliations
1 Department of Electrical and Computer Engineering, Western University, London, Ontario, CA

Abstract

Mobile learning (m-Learning) is the cutting-edge learning platform to really gain traction, driven mostly by the huge uptake in smartphones and their ever-increasing uses within the educational society. Education has long benefitted from the proliferation of technology; however, m-Learning adoption has not proceeded at the pace one might expect. There is a disconnect between the rate of adoption of the underlying platform (smartphones) and the use of that technology within learning. The reasons behind this have been the subject of several research studies. However, previous studies have mostly focused on investigating the critical success factors (CSFs) from the student perspectives. In this research, we have carried out an extensive study of the six factors that impact the success of m-Learning from instructors' perspectives. The results of the research showed that three factors - technical competence of instructors, Instructors' autonomy, and blended learning - are the most important elements that contribute to m-Learning adoption from instructors' perspectives.

Keywords

M-Learning, Critical Success Factor, Instructor Perspectives.

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Jie Xu ¹, Danny Ho ², Luiz Fernando Capretz ¹

Affiliations
1 Department of Electrical and Computer Engineering, University of Western Ontario, London, Ontario, N6A 5B9, CA
2 NFA Estimation Inc., Richmond Hill, Ontario, CA

Abstract

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value.

Keywords

Software Quality Estimation, Software Metrics, Regression, Neural Networks, Fuzzy Logic.

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