Online-Journals.org (International Association of Online Engineering)
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Process Evaluation for Diversified Academic Assessment Mechanism in Higher Education Institutions by Use of Data Mining
Get PDFA diversified academic assessment mechanism can effectively improve students’ learning motivation, make up for the possible blind spots of a single assessment method, and better guide students’ learning and teachers’ teaching. Using data mining methods to process evaluation data for diversified academic assessment mechanisms in colleges and universities can discover patterns in students’ learning, find key factors affecting academic performance, and provide a basis for teaching reform. Most of the current process evaluation data mining methods focus on hard skills, such as academic performance and classroom participation, but it is difficult to evaluate soft skills such as critical thinking and teamwork. To this end, this paper studies the process evaluation data mining methods for a diversified academic assessment mechanism in colleges and universities. It constructs an indicator system for process evaluation of diversified academic assessment mechanism in colleges and universities, gives a quantitative method for indicators, and performs fuzzy comprehensive evaluation based on AHP-entropy weight method. For the evaluation of text-based indicators, a consistency training method is introduced to train the process evaluation correlation mining model using a large amount of unlabeled process evaluation examples, which effectively solves the problems of lack of labeled data, high labeling cost, and changes in data distribution, and improves the performance and availability of the model. The experimental results verify the effectiveness of the proposed method
LRAP: Layered Ring Based Adaptive and Personalized Usability Model for Mobile Commerce Apps
Get PDFUsability is one of the most important characteristics of software applications, especially when it comes to mobile shopping applications. There is a great deal of shift from traditional shopping to online shopping because it benefits both parties i.e., customers as well as businessmen. In such a scenario, the usability factor can play a very vital role in the business industry. If a client stops using a mobile shopping app because it is not user-friendly, it can badly damage the annual revenues especially when hundreds of alternatives are available and there is tough competition. Therefore, to keep existing customers intact and to attract new customers, it is very important to provide a user-friendly mobile app to customers. In this paper, we consider a large variety of online customers with diverse requirements. background and constraints and evaluate the usability of existing mobile e-commerce apps to identify the actual problems people are facing with existing applications and do a systematic review of existing shopping apps. Then, we propose a personalized and adaptive usability model for mobile commerce apps considering the neglected user type i.e., illiterates and people with tactile disabilities. The proposed model LRAP is a layered approach from generalization to specification and it can be considered an extension of the famous PACMAD usability model. Besides this, we also suggest a combining score tool which will be helpful in measuring the usability of any app
The Effect of Interface Instrumentation Experiments-Supported Blended Learning on Students’ Critical Thinking Skills and Academic Achievement
Get PDFThis study aims to promote students’ critical thinking skills and academic achievement using Interface Instrumentation Experiments-supported Blended Learning (IIE-BL). In this quasi-experimental study, 40 undergraduate students who took the Basic Physics II course from the Department of Physics Education at two private universities in Indonesia were assigned randomly as experimental and control groups. Experimental group students were taught using blended learning based on interface instrumentation technology, while the control group students were taught using conventional learning by the same lecturer. To collect data, the Critical Thinking Test (CTT) and Physics Achievement Test (PAT) were administered, which data were then analyzed in independent and paired t-tests, as well as the d-effect sizes test on SPSS 26 at a significance level of 0.05. The results showed that students in the experimental group obtained significantly higher critical thinking skills and achievement scores than students in the control group. It can be concluded that IIE-BL is effective in improving students’ critical thinking skills and achievement in the Basic Physics II course. Educators are suggested to apply IIE-BL to promote student performance and catalyze their learning
Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory Based on the Technology Acceptance Model
Get PDFThis study discusses the analysis of various variables affecting vocational students' intention to use a Virtual Laboratory (VL) in remote learning. Based on the Technology Acceptance Model (TAM), perceived ease of use (PEU), and perceived usefulness (PU) as exogenous variables. At the same time, attitudes towards VL (A) as an intervening variable. This research was conducted in the learning process of the Power Electronics Practicum for vocational education students. This study involved 105 vocational students from the Industrial Electrical Engineering Study Program, at Universitas Negeri Padang. Research data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM). The study results showed that the exogenous variables (PEU, PU, & A) had a significant and positive effect (directly and indirectly through intervening variables) on vocational students' intentions to use the virtual laboratory to support the implementation of remote learning in the Power Electronics Practicum Course. These factors can be considered in determining the appropriate virtual laboratory application to be applied in the learning process
The Role of Digital Story in Promoting Preventive Health Concepts among Primary Stage Students
Get PDFThe study aims to investigate the role of digital stories in promoting the concepts of preventive health among primary stage students. The study tool used to conduct this study is a questionnaire that contains 30 items focusing on six areas, namely health food, risk management, personal health, environmental health, first aid, and physical health. The study sample consists of 218 male and female teachers. The study concluded that digital stories have an average role in promoting the concepts of preventive health in general among primary stage students from the viewpoint of class teachers. The study recommended the necessity of honing the skills of teachers of the primary stage regarding preventive health concepts. In addition, it is recommended that seminars and training courses should be conducted to teach teachers how to present such concepts to students using digital stories.
 
An Efficient System for Diagnosis of Human Blindness Using Image-Processing and Machine-Learning Methods
Get PDFThe two main causes of blindness are diabetes and glaucoma. Routine diagnosis of blindness is based on the conventional robust mass-screening method. However, despite being cost-effective, this method has some problems as a human eye-disease detection method because there are many types of eye disease that are similar or that result in no visual changes in the eye image. These issues make it highly difficult to recognize blindness and control it. Moreover, the color of the macula of the spot can be very close to that of the affected macula in a variety of eye diseases, which suggests that the color of the macula spot can indicate various possibilities, rather than one. This paper discusses the shortcomings of current blindness-screening and monitoring systems and presents a feature-based blindness diagnosis approach using digital eye fundus images for the purpose of automated diagnosis of eye disorders, considering three conditions: healthy eye, diabetic retinopathy (DR), and glaucoma. As such, this paper develops a computer-aided diagnosis (CAD) method for automated detection of human blindness. The proposed approach integrates Gabor filter features, statistical features, colored features, morphological features, and local binary pattern features, then compares them with features drawn from a standard dataset of 1580 fundus images. Several classification techniques were applied to the extracted-features neural network (NN), support vector machine (SVM), naïve bias (NB). SVM classifiers show the most promising accuracy. They achieved 93.3% over the other classifiers
Working with Students with Special Educational Needs and Predictors of Burnout. The Role of ICTs.
Get PDFThe purpose of this study was to examine the burnout dimensions of professionals working with students with special educational needs and the role played by their personal traits in the prevalence of the syndrome. To examine this objective a sample of Greek teachers was selected. The data was collected using the online form of Maslach Burnout Inventory. The results of this research showed that the main prognostic factors of the syndrome in each dimension are the total previous service with students with special educational needs, the specialty, as well as, the age of the sample
Localization of Strangeness for Real Time Video in Crowd Activity Using Optical Flow and Entropy
Get PDFAnomaly detection, which is also referred to as novelty detection or outlier detection, is process of identifying unusual occurrences, observations, or events which considerably differ from the bulk of data and do not fit a predetermined definition of typical behavior. Medicine, cybersecurity, statistics, machine vision, law enforcement, neurology, and financial fraud are just a handful of the industries where anomaly detection is used. In the presented study, an online tool is utilized to identify crowd distortions, which could be brought on by panic. An activity map is produced with the use of numerous frames to show the continuity regarding the flow over time following the global optical flow has been calculated in the quickest time and with the highest precision possible utilizing the Farneback approach to calculate the magnitudes. Utilizing a specific threshold, the oddity in the video will be picked up by the activity map's generation of an entropy. The results indicate that the maximum entropy level for indoor video is <0.16 and the maximum entropy level for outdoor video is >0.45. A threshold of 0.04 is used to determine whether a frame is abnormal or normal
End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization
Get PDFThe automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acoustic features. The conducted experiments on the TIMIT dataset show that the proposed approach outperforms many previous studies on speaker profiling with a mean absolute error (MAE) of 5.18 and 4.91 cm in height estimation and MAE of 5.36 and 6.07 years in age estimation for males and females, respectively, and achieving an accuracy of 99.98% in gender prediction
Static Hand Gesture Recognition Using Novel Convolutional Neural Network and Support Vector Machine
Get PDFHand tracking and identification through visual means pose a challenging problem. To simplify the identification of hand gestures, some systems have incorporated position markers or colored bands, which are not ideal for controlling robots due to their inconvenience. The motion recognition problem can be solved by combining object identification, recognition, and tracking using image processing techniques. A wide variety of target detection and recognition image processing methods are available. This paper proposes novel CNN-based methods to create a user-free hand gesture detection system. The use of synthetic techniques is recommended to improve recognition accuracy. The proposed method offers several advantages over existing methods, including higher accuracy and real-time hand gesture recognition suitable for sign language recognition and human-computer interaction. The CNN automatically extracts high-level characteristics from the source picture, and the SVM is used to classify these features. This study employed a CNN to automatically extract traits from raw EMG images, which is different from conventional feature extractors. The SVM classifier then determines which hand gestures are being made. Our tests demonstrate that the proposed strategy achieves superior accuracy compared to using only CNN