Enhancing the Teaching and Learning Process Using Video Streaming Servers and Forecasting Techniques

Raza Hasan, Sellappan Palaniappan, Salman Mahmood, Babar Shah, Ali Abbas and Kamal Uddin Sarker
Additional contact information
Raza Hasan: Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, 47810 Petaling Jaya, Selangor, Malaysia
Sellappan Palaniappan: Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, 47810 Petaling Jaya, Selangor, Malaysia
Salman Mahmood: Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, 47810 Petaling Jaya, Selangor, Malaysia
Babar Shah: College of Technological Innovation, Zayed University, P.O. Box 144534, Abu Dhabi, United Arab Emirates
Ali Abbas: Department of Computing, Middle East College, Knowledge Oasis Muscat, P.B. No. 79, Al Rusayl Postal Code: 124, Sultanate of Oman
Kamal Uddin Sarker: School of Informatics and Applied Mathematics, University Malaysia Terengganu, 21030 Kuala Terengganu, Terengganu, Malaysia

Sustainability, 2019, vol. 11, issue 7, 1-21

Abstract: Higher educational institutes (HEI) are adopting ubiquitous and smart equipment such as mobile devices or digital gadgets to deliver educational content in a more effective manner than the traditional approaches. In present works, a lot of smart classroom approaches have been developed, however, the student learning experience is not yet fully explored. Moreover, module historical data over time is not considered which could provide insight into the possible outcomes in the future, leading new improvements and working as an early detection method for the future results within the module. This paper proposes a framework by taking into account module historical data in order to predict module performance, particularly the module result before the commencement of classes with the goal of improving module pass percentage. Furthermore, a video streaming server along with blended learning are sequentially integrated with the designed framework to ensure correctness of teaching and learning pedagogy. Simulation results demonstrate that by considering module historical data using time series forecasting helps in improving module performance in terms of module delivery and result outcome in terms of pass percentage. Furthermore, the proposed framework provides a mechanism for faculties to adjust their teaching style according to student performance level to minimize the student failure rate.

Keywords: blended learning; exponential smoothing; learning analytics; learning management system; moodle; prediction; smart classroom; time series forecasting; virtual learning environment; video streaming server; information and communication technology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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