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Engagement is an important factor influencing the effectiveness and quality of online learning programs and how satisfied online learners are with the online learning experience. Therefore, when developing online learning programs, ways to increase engagement should be one of the top priorities. To determine the parameters that increase engagement of independent online learners, this paper selects three types of quantitative data by which to assess online learning engagement: video playing options, video lecture viewing time, and concurrent learning behaviors. This data, collected from real-time observation and analysis of authentic online learning of 14,000 adult learners, was used to run a real-time growth algorithm that determined a few key parameters that increase independent-learning engagement. A few primary results of this study are: 1) real-time, dynamically calculated data representing general or individual engagement parameters of independent learning can help both teachers and learners be aware of, recognize, and adjust learning status accurately and effectively; 2) the algorithm can identify optimal parameters for online learning by analyzing numerical values of engagement, rules, and characteristics of online learning that are difficult to be observed directly; and 3) it is necessary to dynamically analyze learners' engagement based on their learning processes and behaviors in response to different video lectures to know the effectiveness and feasibility of such materials and to support the design, production, and modification of optimal learning materials.

Automatic learning feedback monitoring and analysis are becoming essential in modern education. We present a video analytic system capable of monitoring in-class student’s learning behaviors and providing feedback to the instructor. It is a common practice nowadays for students to take electronic notes or browse online using laptops and cellphones in class. However the use of technology can also impact student concentration and affect learning behaviors, which can seriously hinder their learning progress if not controlled properly. In this pioneering study, we propose a non-intrusive deep-learning based computer vision system to monitor student concentration by extracting and inferring high-level visual behavior cues, including their facial expressions, gestures and activities. Our system can automatically assist instructors with situational awareness in real time. We assume only RGB color images as input and runable system on edge devices for easy deployment. We propose two video analytic components for student behavior analysis: (1) The facial analysis component operates based on Dlib face detection and facial landmark tracking to localize each student and analyze their face orientations, eye blinking, gazes, and facial expressions. (2) The activity detection and recognition component operates based on OpenPose and COCO object detection can identify eight types of in-class gestures and behaviors including raising-hand, typing, phone-answering, crooked-head, desk napping, etc. Experiments are performed on a newly collected real-world In-Class Student Activity Dataset (ICSAD), where we achieved nearly 80% activity detection rate. Our system is view-independent in handling facial and pose orientations with average angular error < 10°. The source code of this work is at: https://github.com/YiZengHsieh/ICSAD. [ABSTRACT FROM AUTHOR]

Exercise monitoring systems for rehabilitation are usually not able to pinpoint the exact part for patients’ exercise. The research objective is to develop the projection-based motion recognition (PMR) algorithm based on depth data and wide-accepted methods to solve this matter. We regard a motion trajectory as a combination of basic posture units, and then project the basic posture units onto a 2-D space via a projection mapping. Each motion trajectory is transformed to a 2-D motion trajectory map by sequentially connecting the basic posture units involved in the motion trajectory. Finally, we employ a convolutional neural network (CNN)-based classifier to classify the trajectory maps. Accurate classification rate reaches as high as 95.21%. The originality of PMR algorithm lies in (1) it has the generalization capability to some extent since it only adopts popular methods and contains an essential and comprehensive mechanism; (2) the resultant trajectory map may reveal the information about how well a patient execute the rehabilitation assignments. [ABSTRACT FROM AUTHOR]

The authors developed a self-organizing map-based optimization (SOMO) neurofuzzy classifier to deal with a practical expatriation willingness (EW) problem, which is associated with employees’ willingness to accept expatriate assignments. The proposed model also delivers more information about rule coverage and generates user-friendly outcomes. The authors adopt the SOMO algorithm to optimize the weights of neurons in the proposed hybrid neurofuzzy classifier. They evaluated the model’s feasibility using the databases from previous studies exploring employees’ EW. The results show that the proposed neurofuzzy classifier yields 11 determination rules whose accuracy rates are greater than 80 percent. The contribution to the body of knowledge lies in the significant improvement in accuracy rates and coverage for predicting EW rules, optimization for all the network parameters using a novel algorithm, and more friendly outputs for practical use. [ABSTRACT FROM PUBLISHER]

Features a study on the model of self-organizing neural networks. Details on the self-organizing feature map algorithm; Background of applying feature maps in clustering data; Description of the self-organizing neural network model; Simulation results; Conclusion.

Presents a study which proposed an approach to forming feature map algorithm. Goal of the Kohonen self-organizing map algorithm; Simulation results; Conclusion.

Provides information on a study which developed a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements. Hybrid training algorithm; Simulation results; Conclusion of the study.

Demonstrates how neuro-fuzzy networks can be constructed and used on-line for monitoring voltage security. Details on the proposed layers of Fuzzy Hyperrectangular Composite Neural Networks (FHRCNN); Features of neuro-fuzzy networks; How the efficacy of the network was tested.