Enhancing the teaching and learning process using video streaming servers and forecasting techniques
Source of Publication
© 2019 by the authors. 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.
Blended learning, Exponential smoothing, Information and communication technology, Learning analytics, Learning management system, Moodle, Prediction, Smart classroom, Time series forecasting, Video streaming server, Virtual learning environment
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Hasan, Raza; Palaniappan, Sellappan; Mahmood, Salman; Shah, Babar; Abbas, Ali; and Sarker, Kamal Uddin, "Enhancing the teaching and learning process using video streaming servers and forecasting techniques" (2019). All Works. 1506.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series