A Novel Study of the Relation Between Students Navigational Behavior on Blackboard and their Learning Performance in an Undergraduate Networking Course

This paper provides an overview of students behavior analysis on a learning management system (LMS), Blackboard (Bb) Learn for a core data communications course of the Undergraduate IT program in the Information Sciences and Technology (IST) Department at George Mason University (GMU). This study is an attempt to understand the navigational behavior of students on Blackboard Learn which can be further attributed to the overall performance of the students. In total, 160 undergraduate students participated in the study. Vast amount of students activities data across all four sections of the course were collected. All sections have similar content, assessment design and instruction methods. A correlation analysis between the different assessment methods and various key variables such as total student time, total number of logins and various other factors were performed, to evaluate students engagement on Blackboard Learn. Our findings can help instructors to efficiently identify students strengths or weaknesses and fine-tune their courses for better student engagement and performance

Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine

The advent of Learning Management System (LMS) has unfolded a unique opportunity to predict student grades well in advance which benefits both students and educational institutions. The objective of this study is to investigate student access patterns and navigational data of Blackboard (Bb), a form of LMS, to forecast final grades. This research study consists of students who are pursuing a Networking course in Information Science and Technology Department (IST) at George Mason University (GMU). The gathered data consists of a wide variety of attributes, such as the amount of time spent on lecture slides and other learning materials, number of times course contents are accessed, time and days of the week study material is reviewed, and student grades in various assessments. By analyzing these predictors using Support Vector Machine, one of the most efficient classification algorithms available, we are able to project final grades of students and identify those individuals who are at risk for failing the course so that they can receive proper guidance from instructors. After comparing actual grades with predicted grades, it is concluded that our developed model is able to accurately predict grades of 70% of the students. This study stands unique as it is the first to employ solely online LMS data to successfully deduce academic outcomes of students

Nano-particle deposition in the presence of electric field

The dispersion and deposition of nano-particles in laminar flows in the presence of an electric field were studied. The Eulerian-Lagrangian particle tracking method was used to simulate nano-particle motions under the one-way coupling assumption. For nano-particles in the size range of 5–200 nm, in addition to the Brownian excitation, the electrostatic and gravitational forces were included in the analysis. Different charging mechanisms including field and diffusion charging as well as the Boltzmann charge distributions were investigated. The simulation methodology was first validated for Brownian and electrostatic forces. For the combined field and diffusion charging, the simulation results showed that in the presence of an electric field of 10 kV/m, the electrostatic force dominates the Brownian effects. However, when the electric field was 1 kV/m, the Brownian motion strongly affected the particle dispersion and deposition processes. For the electric field intensity of 1 kV/m, for 10 nm and 100 nm particles, the deposition efficiencies for the combined effects of electrostatic and Brownian motion were, respectively, about 27% and 161.2% higher than the case in the absence of electric field. Furthermore, particles with the Boltzmann charge distribution had the maximum deposition for 20 nm particles

Evaluation of residence time on nitrogen oxides removal in non-thermal plasma reactor

Non-thermal plasma (NTP) has been introduced over the last few years as a promising after- treatment system for nitrogen oxides and particulate matter removal from diesel exhaust. NTP technology has not been commercialised as yet, due to its high rate of energy consumption. Therefore, it is important to seek out new methods to improve NTP performance. Residence time is a crucial parameter in engine exhaust emissions treatment. In this paper, different electrode shapes are analysed and the corresponding residence time and NOx removal efficiency are studied. An axisymmetric laminar model is used for obtaining residence time distribution numerically using FLUENT software. If the mean residence time in a NTP plasma reactor increases, there will be a corresponding increase in the reaction time and consequently the pollutant removal efficiency increases. Three different screw thread electrodes and a rod electrode are examined. The results show the advantage of screw thread electrodes in comparison with the rod electrode. Furthermore,between the screw thread electrodes, the electrode with the thread width of 1 mm has the highest NOx removal due to higher residence time and a greater number of micro-discharges. The results show that the residence time of the screw thread electrode with a thread width of 1 mm is 21% more than for the rod electrode

An investigation of nano-particle deposition in cylindrical tubes under Laminar condition using Lagrangian transport model

Aerosol deposition in cylindrical tubes is a subject of interest to researchers and engineers in many applications of aerosol physics and metrology. Investigation of nano-particles in different aspects such as lungs, upper airways, batteries and vehicle exhaust gases is vital due the smaller size, adverse health effect and higher trouble for trapping than the micro-particles. The Lagrangian particle tracking provides an effective method for simulating the deposition of nano-particles as well as micro-particles as it accounts for the particle inertia effect as well as the Brownian excitation. However, using the Lagrangian approach for simulating ultrafine particles has been limited due to computational cost and numerical difficulties. In this paper, the deposition of nano-particles in cylindrical tubes under laminar condition is studied using the Lagrangian particle tracking method. The commercial Fluent software is used to simulate the fluid flow in the pipes and to study the deposition and dispersion of nano-particles. Different particle diameters as well as different flow rates are examined. The point analysis in a uniform flow is performed for validating the Brownian motion. The results show good agreement between the calculated deposition efficiency and the analytic correlations in the literature. Furthermore, for the nano-particles with the diameter more than 40 nm, the calculated deposition efficiency by the Lagrangian method is less than the analytic correlations based on Eulerian method due to statistical error or the inertia effect