Online-Journals.org (International Association of Online Engineering)
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Utilizing Machine Learning Techniques to Predict University Students' Digital Competence
Get PDFGiven the importance of possessing the digital competence (DC) required by the technological age, whether for teachers or students and even communities and governments, educational institutions in most countries have sought to benefit from modern technologies brought about by the technological revolution in developing learning and teaching and using modern technologies in providing educational services to learners. Since university students will have the doors to work opened in all fields, the research aims to know their level of DC in artificial intelligence (AI) applications and systems utilizing machine learning (ML) techniques. The descriptive approach was used, as the research community consisted of students from the University of Baghdad in its colleges with scientific and human specializations. To measure the level of DC, a questionnaire was applied as a data collection tool to a sample of 400 male and female students, distributed based on gender and academic specialization. The results showed that the sample students did not have high DC. Their possession of DC related to AI applications and systems was to a moderate degree. The results indicated that there were differences in the responses of the study sample members due to the gender variable and the specialization variable, in favor of the female students with scientific specialization
Mobile Application of Inductive Multi-Strategies for Virtual Teaching of Research to Engineering Students
Get PDFThe university curriculum requires the implementation of various strategies for research training. Accordingly, the present study aims to implement a multi-strategy mobile application for the teaching of research by engineering students of a private Peruvian university. Using a mixed methodology, in the first study, action research was applied with the participation of four teachers and two academic coordinators. Management documents such as the University Law and curricular documents such as syllabi were analyzed. Thus, a multi-strategy learning session was designed and tested according to SINEACE standards. Then, in the second quantitative study, the proposed sessions suggested by the teachers were applied to a sample of 167 students of Systems Engineering, Mechanical Engineering, Civil Engineering, and Industrial Engineering, who achieved “excellent progress” in conceptual skills and “good progress” in procedural skills for research
Leveraging Analytics to Drive Human Performance
Get PDFRecent audit reports by the Auditor General noted the potential impact to Canadian Armed Forces readiness due to sub optimal Defence Supply Chain performance. To identify the root cause of performance deficiencies and assess the adequacy of existing training, a systematic approach was employed to identify the knowledge and skills required by each Supply Chain role to perform their share of tasks across 39 Processes. Based on the Department of National Defence Supply Administration Manual (SAM), the project team mapped Processes and Tasks to all applicable Supply Chain Phases. Processes and Tasks were also mapped to each role and the training priority was determined based on Difficulty, Importance and Frequency (DIF) analysis. We then mapped topics/teaching points from relevant course to existing processes and Tasks; and generated a list of processes and Tasks with “adequate”, “limited” or “no” curriculum to support the acquisition of requisite knowledge and skills for each role. The analysis revealed:
• All roles contribute heavily to the overall success of the Supply Chain in an integrated work environment – necessitating an understanding of the impact of their work on others. • Developing curriculum incrementally over the years based on specific, sometimes narrow needs/performance and without a comprehensive map as outlined above yielded inefficient learning solutions. • Developing role-based solutions in parallel with process-based curriculum resulted in gaps and duplication of effort.
This paper reaffirms the need for “getting back to basics”. A thorough analysis and mapping of actual work/role requirements based on an authoritative reference, using a systematic process enabled by a leading-edge Training Management System, will provide a robust analysis framework. Training gaps and overlaps will become evident, and a blueprint for a comprehensive re-organization of the curriculum will naturally emerge
Exploring Essential Acoustic Features for Early Parkinson’s Disease Classification: A Machine Learning Study
Get PDFParkinson’s disease (PD) is a neurological condition that affects approximately 10 million individuals globally and is ranked as the second most prevalent neurodegenerative condition after Alzheimer’s disease. Vocal disorders can be identified in approximately 90% of PD patients in the early stages of the disease. In this study, 19 machine learning (ML) algorithms were applied to a database of voice recordings of healthy individuals and individuals with PD obtained from a public repository. Different feature selection (FS) and hyperparameter optimization techniques were applied to all models for training, testing, and validation data. Among the ML algorithms, support vector machine with radial kernel, Naïve Bayes (NB), and Gaussian process classifier (GPC) yielded promising results when considering all features. Linear discriminant analysis, K neighbors classifier (KNN), extra trees classifier, GPC, and NB demonstrated excellent performance on the testing data after employing FS techniques. Decision tree classifier, KNN, and GPC emerged as the top performers when applied to the validation dataset. Our findings, derived from an extensive and chronological review of studies utilizing the same dataset, which surpass previous benchmarks, provide a comprehensive understanding of ML’s application in voice analysis to support accurate clinical decision-making
Convolutional Neural Network with Feature Extraction to Improve the Classification Accuracy of Multi-Class Facial Skin Disorders
Get PDFThis study aims to improve the accuracy of multi-class facial skin disorder classification using a convolutional neural network (CNN) enhanced with feature extraction. The CNN method for classifying multi-class facial skin disorders uses color feature extraction using color moment (CM) and Laplacian of Gaussian (LoG) for direct shape with image data. Multi-class facial skin disorders include oily, hyperpigmentation, acne, redness, blackhead, and normal. A public dataset is used with 7151 images with a balanced number of data classes. Researchers divided the data set into 80% for training and 20% for testing. Experiments are carried out through training and testing with 100 epochs, resulting in an accuracy of 85% for CNN, 66% for the CM-CNN, 80% for LoG-CNN, and 91% for CM-LoG-CNN. The highest classification accuracy is achieved with the CM-LoG-CNN combination
Augmented Reality with the Cognitive Conflict Model: What Is Effective for Improving Students' Scientific Literacy of Dynamic Fluid Material?
Get PDFThis study aimed to develop an augmented reality (AR) application by integrating the cognitive conflict-based learning (CCBL) model to enhance students’ scientific literacy in a dynamic fluid material. The study followed a design or development framework utilizing the Plomp model stages, including design, validity, practicality, and effectiveness tests. In the effectiveness test phase, a quasi-experimental design was applied with control and experimental groups. The control group consisted of 36 students who learned through traditional methods, while the experimental group included 36 students who learned using AR. The application design characteristics incorporated the stages of the CCBL model, presenting AR at each stage. The validity test of the AR application yielded a score of 0.95, which was categorized as valid. The practicality test scored 94% in the one-to-one stage and 95.7% in the small group stage, categorized as practical. The effectiveness test demonstrated that AR-based learning was superior to traditional learning methods in improving students’ scientific literacy regarding dynamic fluid material. Overall, the AR application designed with the characteristics of the CCBL model stages was validated, practical, and effective, representing an innovative solution for improving students’ scientific literacy in physics education
Advanced Deep Learning Integration for Early Pneumonia Detection for Smart Healthcare
Get PDFThe surveillance of symptoms related to pneumonia is increasingly crucial due to its widespread occurrence and similarity to symptoms exhibited in other contagious diseases such as influenza, respiratory syncytial virus (RSV), and COVID-19. The timely identification of pneumonia can significantly diminish mortality rates. To tackle this issue, a pioneering non-contact technique for monitoring pneumonia symptoms has been developed. This investigation mainly concentrates on the early detection of pneumonia, which can serve as an indicator of the onset of other persistent ailments. The technique entails an analytical approach to scrutinize symptoms such as cold, cough, chills, sore throat, altered respiratory rates, and elevated body temperature by employing depth imaging methods. The crux of this exploration lies in the utilization of a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks to classify video images in order to identify symptoms associated with pneumonia. The proposed model has showcased an impressive overall accuracy rate of 98.02% along with a significantly optimized prediction time of a mere 8.63 ms. Moreover, the study encompasses a comprehensive evaluation of various deep learning techniques in the detection of diseases exhibiting symptoms akin to pneumonia. The study introduces a pivotal advancement in medical diagnostics, emphasizing the importance and effectiveness of a fusion-based, profound learning system in the non-contact identification of pneumonia symptoms. This innovative approach has the potential to revolutionize the way pneumonia and similar diseases are diagnosed and monitored
Evaluating Cognitive Aspects of ADHD Students Using Brain-Computer Interface and a Digital Game: A Study in Brazil
Get PDFIn an academic setting, attention deficit hyperactivity disorder (ADHD) is a significant risk factor for suboptimal academic performance. A novel approach to the detection and treatment of children with ADHD is the brain-computer interface (BCI). The objective of this study was to develop and evaluate a computerized cognitive test (CCT) using a digital game and a BCI to identify and analyze differences in cognitive performance between students with and without ADHD. A qualitative-quantitative experimental study was conducted with 20 students’ aged 8 to 14, divided into two groups: 1) 10 with ADHD and 2) 10 without ADHD. At the outset of the study, a semi-structured interview was conducted to obtain an initial understanding of the participants’ backgrounds and medical histories. Subsequently, neuropsychological tests were administered for the purpose of assessing cognitive levels. Following this, the BCI was used in conjunction with the digital game. The neuropsychological instruments yielded statistically significant results between the two samples. The results of the CCT indicated that students without ADHD exhibited superior performance in eight of the nine analyzed criteria. The utilization of a digital game integrated with a BCI can assist in the identification of cognitive aspects that distinguish children with and without ADHD. Moreover, individuals diagnosed with ADHD demonstrated enhanced performance across all phases of the game
Digital Inclusion and Accessibility through Augmented Reality Mobile Technologies in Education: A Systematic Review
Get PDFAugmented reality (AR) is emerging to revolutionize education through the simulation of environments, transforming traditional teaching methods. This systematic literature review (SLR) rigorously explores the factors that influence the digital inclusion and accessibility of augmented reality technologies in education during the period 2022–2024. To carry out the research, the guidelines proposed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method were followed, ensuring consistency in the information collected. Four highly relevant databases were considered, such as Science Direct, Scopus, Web of Science, and IEEE, taking into account suitability criteria, identifying 28 articles and 27 conferences in paper structure. The results show that 2023 was the year with the highest number of publications, with 45.45%. Also, among the prominent countries, India showed a remarkable predominance with 12.73% of manuscripts. In addition, the research showed that original articles accounted for 51%, highlighting their relevance over conferences. It is important to note that 72% of the manuscripts used for this review correspond to a quantitative approach, 16% to a qualitative approach, and 12% to a mixed approach. Finally, it is concluded that the study allowed us to contrast the difficulties faced by educational institutions in integrating RA, considering the scope and relevant aspects in research aligned to the object of discussion
Teachers' Perspectives on Using Technology to Facilitate Pupil Participation
No full textIn the current digital era, technology plays a crucial role in facilitating diverse interactions that are essential for pupil engagement in the learning process. This article delves into the perspectives of secondary school teachers regarding the active participation of pupils in school activities through the utilization of digital technologies. The concept of participation is approached from two dimensions: passive presence and active involvement, with a specific focus on the latter, emphasizing active engagement. The study centers on the application of digital resources in Swedish grades 7–9 to promote pupil participation and enhance the learning experience. We examine the use, effectiveness, and areas requiring improvement of existing digital resources. Drawing from prior workshops involving teachers, we aim to elucidate educators’ viewpoints on the role of technology in enhancing pupil participation. Grounded in the Garrison and Andersson’s theoretical framework, the study advances the comprehension of the interactions necessary to foster an effective learning environment, as perceived by educators. The results derived from the thematic analysis yield four themes: 1) Interaction between teacher and pupil, 2) Interaction between pupil and content, 3) Interaction between pupil and pupil and 4) Extended interaction. The study concludes by outlining a set of guidelines in how digital resources can support pupil participation as the response to identified challenges