Opinion Prediction of Hungarian Students for Real-Time E-Learning Systems: A Futuristic Sustainable Technology-Based Solution

This work is a new step towards the understanding of students’ opinions about the use of technology in learning and improvements to provide sustainable E-learning solutions. Every higher educational university tries to provide well-suited, updated, and trending technology-based education facilities to its students. The task of analyzing the student’s sentiment about technology delivers benefits not only to ICT administrators, but also to management to become aware of the technological concerns. The opinions of Hungarian university students were analyzed using the regression method. We investigated 165 primary samples supported by the four hypotheses. The reliability of the data sample was calculated as 0.91 with Cronbach alpha testing. The Pearson Momentum Correlation (PMC) proved that the suggested technology benefits had a linear positive association with the student’s opinion. Furthermore, technology usability was positively correlated with the benefits. The supporting results of the regression model evidenced the significant impact of technology usability and benefits on the opinions. Using Exploratory Factor Analysis (EFA), we proposed significant features for the model that predicted students’ opinions using the educational benefit and usability parameters. These parameters statistically significantly predicted student’s opinions: F (2, 162) = 104.9, p < 0.05, R2 = 0.559. This study may be supportive of implementing the opinion mining model online and useful to university authorities to understand better the students’ sentiments about the current technological facilities provided. The authors proposed an opinion mining model to deploy on the university’s real-time “E-lection” sustainable technology.

A Computational Fluid Dynamic Study of a Momentum and Energy Method on Rough Surfaces

The Discrete Element Roughness Method (DERM) has been used to improve convective heat transfer predictions on surface roughness. This work aims to validate the core momentum-correlation of DERM through evaluating Computational fluid dynamics (CFD)-based solution of the flow around individual roughness elements with the goal of improving the correlations. More specifically, the matrix of scenarios evaluated using includes three different roughness elements at three different pressure drops (or flow rates). Results from these studies are to be used to validate and improve correlations used to approximate roughness in DERM. For further comparison, a fourth roughness element analyzed in previous work will also be compared. For each element, a steady and unsteady case are conducted and analyzed. The momentum loss results obtained from the CFD are then compared to the DERM-based predictions from the same roughness elements in search of any discrepancies. It is observed the momentum-correlation deviates from the CFD prediction with increasing element height.