Online-Journals.org (International Association of Online Engineering)
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A New Highly Portable Simulator (SECMA) Based on Virtual Reality for Teaching Essential Skills in Minimally Invasive Surgeries
Get PDFThis study presents a new minimal access surgery training system, SECMA, and its constructive validation to determine its usefulness for training basic laparoscopic skills. SECMA is an affordable, highly portable, mobile virtual reality training tool for laparoscopic techniques that integrates the Oculus Quest with a mechanical interface for surgeon simulation of forceps using the hand controllers of these devices. It allows the execution of structured activities (supported by virtual scenarios simulating operating rooms developed in Unity), performance evaluation, and real-time data capture. Two experiments were carried out: 1) coordination; and 2) capture and transport, with a total of 21 individuals divided into two groups: a novice group (inexperienced) of 10 participants and an expert group (>100 endoscopic procedures) of 11 participants. Total task time score, right-hand speed, path length, and other metrics from several consecutive runs on the simulator were compared between experts and novices. Data automatically recorded by SECMA during the experiments were analyzed using hypothesis tests, linear regressions, analysis of variance, principal component analysis, and machine learning-supervised classifiers. In the experiments, the experts scored significantly better than the novices in all the parameters used. The tasks evaluated discriminated between the skills of experienced and novice surgeons, giving the first indication of construct validity for SECMA
Effective Brain Stroke Prediction with Deep Learning Model by Incorporating YOLO_5 and SSD
Get PDFIschemic stroke is a life-threatening disorder that significantly reduces a person’s lifespan. The timely diagnosis of stroke heavily relies on medical imaging techniques such as magnetic resonance imaging (MRI), computerized tomography (CT), and x-ray imaging. However, the manual localization and analysis of these images can be time-consuming and yield less accurate results. To address this challenge, we propose the implementation of deep-learning object detection techniques for computerized lesion identification in medical images. In this study, we employ three categories of deep learning object identification networks: deep convolutional neural network (DCNN), you only look once (YOLO) 5, and single-shot detector (SSD). By leveraging these advanced deep learning models, we aim to reduce the effort and time required for screening and analyzing a significant number of daily medical images, including MRI, CT, and x-ray images. With the addition of YOLO5 and SSD among these networks, the accuracy achieved was 96.43%, demonstrating their effectiveness in accurately identifying lesions associated with ischemic stroke
A Boosted Evolutionary Neural Architecture Search for Timeseries Forecasting with Application to South African COVID-19 Cases
Get PDFIn recent years, there has been an increase in studies on time-series forecasting for the future occurrence of disease incidents. Improvements in deep learning approaches offer techniques for modelling long-term temporal relationships. Nonetheless, this design practice is rigorously painstaking, prone to errors, and requires human expertise. The advent of feature enrichment with automatic architecture search typically optimises the discovery of new neural architectures applicable in domains such as time-series modelling. The main methodological contribution of this study is an approach for time-series forecasting using feature-enriched filters and an evolutionary neural architecture search with sequence-to-sequence gated recurrent units (GRU-Seq2Seq). This is applied to the prediction of daily cases of coronavirus disease in South Africa. The highly pathogenic coronavirus pandemic incident data was modelled with filters, optimised hyper-parameter search trials and an evolutional neural algorithm. The proposed model was benchmarked against ARIMA and SARIMA. The model predicted trends for 30, 60 and 90-day horizons and evaluated them for 7, 14 and 31 days. Simulation results demonstrate that observed daily case counts with added filters and evolutionary search optimisation for forecasting improve performance accuracy. Generally, the proposed bFilter+GRU-Seq2Seq with optimal search configuration outperformed ARIMA and SARIMA with lower error scores and higher performance metrics, with an R2 score of 7.48E-01 for a 30-day forecast horizon
Development of a Curriculum Management System for a State University in the Philippines
Get PDFCurriculum development is a key to delivering quality education in a university’s offered programs. Bulacan State University (BulSU)’s College of Information Technology (CICT) is known for producing globally competitive graduates in the Philippines. With its program offerings that advance from time to time, CICT shall continue to review, enhance, and revise its curricula to address the rapidly changing needs of the nation and the expected population it serves. The study utilized descriptive developmental methodology. This study focused on the digitization of the curriculum development process at CICT, allowing stakeholders, such as faculty members, students, industry, and academic partners, to review the college’s curriculum for the coming years. The system was found acceptable by its intended users and will soon improve the curriculum development process at the college upon implementation
Hybrid Learning through Lecture Capture: Exploring Perspectives and Overcoming Challenges with Blackboard Encore Technology
Get PDFThe COVID-19 pandemic has resulted in significant disruptions in higher education. Despite UK universities returning to in-person teaching, many international students still face travel restrictions imposed by their home countries, resulting in remote learning for many students at the beginning of the 2022–2023 academic year. To ensure a seamless academic transition for all students, a British university has implemented a hybrid learning model that utilizes lecture capture technology, specifically Blackboard Encore Streaming, to provide face-to-face and remote students with access to the same learning experience. This paper presents the findings from two online focus groups with students (n = 18) and two staff reflections, which aimed to explore the perspectives of teachers and students on the new Encore Streaming feature. The results indicate that both teachers and students value the hybrid approach, with remote students appreciating the flexibility and convenience of this approach more than face-to-face students. However, the study also reveals significant discrepancies in teacher attention and student participation opportunities between remote and face-to-face students, attributed to pedagogical and technological barriers. The study highlights the crucial role of teachers in orchestrating the hybrid classroom to ensure equal learning opportunities for all students and identifies areas where teachers require support, such as training, staff time, and technology support
Enhancing Students’ Linear Algebra I Learning Using Assessment Through STACK
Get PDFE-learning is seen as a new philosophy of education that encompasses all existing forms of education, including full-time education. Currently, there is a wide variety of software for developing interactive content. The aim of this study is to improve students’ learning of Linear Algebra I using STACK (the system for teaching and assessment using a computer algebra kernel) at Bahir Dar University. The experience of using STACK questions has shown that their application helps students of all forms of education in learning Linear Algebra I and greatly facilitates the learning process of students, allowing them to master the contents of Linear Algebra I. In a mastery quiz, students try different algebra content repeatedly until they understand the concept. After students have mastered the content, they take a test quiz. Moreover, STACK helps teachers in a scoring (cumulative) system of knowledge assessment, makes the learning outcomes more visible and convenient for analysis. In addition, it is noted in the research that the STACK tasks allow students to review analytical solutions to complex types of problems and organize hints that help them to solve tasks. The approach saves the teacher’s time to check solutions at any time and give students individual options for tasks. As part of the study, the STACK tasks were analysed, which revealed a greater number of advantages of learning and assessment compared to its disadvantages and revealed the possibility of its application in the educational process. It is recommended that educational policy makers must integrate STACK into a curriculum of Mathematics
Data-Driven Prediction of Students’ Online Learning Needs and Optimization of Knowledge Library Management
Get PDFThanks to the advancement of information technology, online learning has become a crucial tool of modern education, and the management of modern education is facing the challenges of how to effectively predict students’ learning needs and how to optimize the management of the knowledge library to support these needs. However, existing data-driven prediction approaches are flawed in handling complex learning environments and timely adapting to changes, so this study attempts to solve these questions by exploring the correlation between learning needs, the extraction of knowledge linkages, and the optimization of knowledge libraries based on rule updates. In our work, a new method was proposed for extracting learning needs and knowledge linkages to more accurately identify and predict students’ learning needs, and a rule-based knowledge library management optimization method was introduced to allow the knowledge library to more flexibly adapt to students’ learning needs and the changes in educational resources. In this way, this study offers a comprehensive and flexible solution for education management via the combination of these two aspects, which not only increases student satisfaction and improves teaching quality but also reduces resource waste and gives students a more personalized and efficient learning experience. Furthermore, the methods and findings of this study could also be used as references for data-driven decision-making in other fields
Virtual Site Visits: Student Perception and Preferences Towards Technology Enabled Experiential Learning
Get PDFSite visits are a key pedagogical tool within natural science and geographical education. Site visits provide an interactive experience to enable learning through the exposure to a real-world spatio-temporal environment. COVID-19 restrictions required the development of a virtual site visit for a landscape ecology course in North America. In this study, a series of digital tools were coordinated to deliver site visit information focusing on multi-sensory, multi-scalar, and multi-media information based on Kolb’s experiential learning model, particularly Step 1, the concrete experience. This research explored student’s perceptions and opinions of the digital tools provided to complete their ecological restoration management assignment and their effectiveness and usability. 4th year natural resource and environmental science students (n=52) reported predominately positive attitudes towards the use of the virtual site visit. Though students did not prefer the virtual site visit over a physical site visit, they noted that the virtual site visit digital tools did provide the same information as a site visit and that they felt they were able to understand all aspects of the physical site through the virtual site visit tools provided, particularly through the digital photographs and the 360-degree virtual reality imagery. Successful student assignments illustrated experiential learning outcomes were met
What Matters the Most? Exploratory Analysis of Environmental and Situational Variables Influencing Performance of Students During COVID-19 Pandemic
Get PDFThis study examines the impact of gender and age differences on the performance of students from different Hungarian universities and colleges in online learning during the third wave of COVID-19. The survey responses were assessed using Partial Least Squares estimation technique. The research model attempts to understand the influence of environmental and situational variables (i.e., compatibility, accessibility, perception of online self-efficacy, mobility) on performance and satisfaction with online education. Apart from mobility, other indicators have significant impact on respondents’ performance. However, moderating effect of age and gender almost do not influence the performance of surveyed Hungarian students. The results demonstrate that gender impacts the compatibility → performance pathway. The age of respondents has no effect on relationships between environmental and situational variables and performance
Conceptual Statistical Assessment Using JSXGraph
Get PDFTraditionally online assessments tend to focus on topics that require students to input algebraic and numeric responses. As such there is a paucity of questions that test students' knowledge of statistics, and what questions there are in our experience focus on computing specific values (mean, standard deviation, and so on). Through making use of a technology called JSXGraph that is supported within the STACK environment for online assessment of mathematical knowledge, we have developed statistics questions that aim to test conceptual knowledge. For example, by requiring students to adjust the bars in a graph in order to produce a dataset that has a required mean, median, mode and range. With careful design this approach enables open-ended questions that have more than one correct answer. In this paper we describe the questions we have designed, and report responses from a sample of students