International Journal of Modern Education and Computer Science @ijmecs
Journal articles - International Journal of Modern Education and Computer Science
All articles: 1186
Evaluation: The New Philosophical Roles & Psychological Means
Scientific article
Evaluation Roles and Means are very broad. It concerns with n number of attributes. This paper discussed the relationship between evaluation & research, philosophy of evaluation, physiology of evaluation, Evaluation in applied psychology. In the above context, how the basic logic of evaluation is set with evaluation fields and the phases of evaluation process.
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Evolution and Future Generation of TV
Scientific article
No other electronic media has created as much mass impact as the TV. TV is both a personal as well as family/community device which makes it reach a large population. Obviously the immense popularity of TV has resulted in an unprecedented growth of TV viewing as well as technology. From the simple TV today one can have a smart TV with varying features satisfying all sections of society. The TV technology has grown in all aspects namely the TV studio technology, the TV transmitter & broadcast technology and the TV receiving device technology. Not only have the TV signals been converted from analog to digital, today one has high definition TV, the IPTV, the mobile TV and the 3D TV commercially available. It is very interesting and important to trace the evolution of TV technology from its basic form as in 1930s to date and to visualize their technical features at various stages of developments. This paper gives an overview of the developments in TV technology highlighting their important features.
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Scientific article
This study analyzes the artificial intelligence (AI) literacy levels of music education students and explores how future music educators perceive and integrate AI tools within creative and instructional contexts. The research group consisted of 143 students enrolled in music education programs from different faculties, such as Education, Fine Arts, and Conservatory. Participants were selected through a purposeful sampling technique, ensuring inclusion of individuals likely to encounter AI in educational and performance settings. The study employed a causal-comparative (ex post facto) design, which enables examination of naturally existing groups differing in characteristics such as prior exposure to or familiarity with AI technologies. Quantitative data were analyzed to compare students’ AI literacy levels across demographic variables, including gender, type of high school graduated from, academic achievement level, and prior knowledge of AI. However, it was determined that the artificial intelligence literacy level of music teacher candidates who have knowledge about artificial intelligence programmes and use artificial intelligence programmes in music education is significant. Results indicated that students’ AI literacy levels were at a medium level. No significant differences were found in AI literacy by gender, type of high school graduated from, academic achievement, or frequency of internet use. However, significant differences were observed among students from different faculties, as well as among those who had prior knowledge of AI programs, used AI applications in music and music education, and used them more frequently. These findings suggest that direct engagement with AI tools contributes to higher literacy levels.
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Examining Chen and Starosta's Model of Intercultural Sensitivity in the Taiwanese Cultural Context
Scientific article
The main purpose of this study was to empirically examine Chen and Starosta's Model of Intercultural Sensitivity and reproduce a valid scale in the Taiwanese cultural context, using both exploratory and confirmatory factor analyses. Results indicated that Chen and Starosta's five-factor model of intercultural sensitivity (IS) did not fit the Taiwanese cultural context. Instead, a four-factor model of IS was created using an exploratory factor analysis. The four factors were based on the 13 items of 24-item Intercultural Sensitivity Scale (ISS) formulated by Chen and Starosta. The reliability coefficient was .801, demonstrating high internal consistency. A confirmatory factor analysis was performed again to determine the construct validity of the alternative model of IS. Since cultural differences may influence the factor structure of a test, using both EFA and CFA can methodologically provide a meaningful explanation for replication studies. This study proposes an alternative model of the Intercultural Sensitivity Scale that is a better fit with Taiwanese culture by reinterpreting Chen and Starosta's ISS.
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Examining Mindfulness in Education
Scientific article
Despite the availability of numerous learning opportunities ranging from face-to-face to computer-based learning, there is need for better understanding of how to support the development of cognitive skills in students. Research has shown that cultivation of mindfulness skills help to develop cognitive skills such as retention, thinking, problem solving, and emotional balance. However, there is only limited research on the effect of mindfulness training in educational settings. We examined cognitive abilities of university students as identified in Bloom's taxonomy and mindfulness skills during a single traditional face-to-face class room session. We hypothesized that mindfulness is a specific cognitive ability that supports the development of other cognitive skills. This pilot study included 148 students from undergraduate and postgraduate programs at two universities in Sri Lanka. The study assessed cognitive abilities, including retention, thinking, out-of-the-box thinking, note-taking and mindfulness at the end of a one-hour lecture. The results showed that students' self-reported mindfulness following a lecture was significantly lower than other cognitive abilities. These results suggest conducting a more formal controlled experiment to investigate the effect of mindfulness training in education.
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Scientific article
Indigenous Knowledge (IK), can be preserved using Information Systems in order to protect cultural heritage and disseminate local knowledge for development. This knowledge often passed on orally for generations has become significant in searching for answers to several world's critical problems, are at risk of becoming extinct. This "traditional wisdom" is highly useful in solving complex problems of health, agriculture, education, use of natural resources and the environment. The main challenges of IK are inadequate documentation and diminishing transmission channels. Both descriptive and quantitative methods are used in this study that focuses on highlighting the importance of indigenous knowledge in the sustainable development process and illustrating ways in which technology can be used to preserve it, thereby enriching the development process from a holistic perspective. This research strengthens the preservation of local IK, enhances its adoption in the formal educational settings, leads to improvement in scientific knowledge development and inspire sustainable community development using a holistic approach.
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Experiences from Video Lectures in Software Engineering Education
Scientific article
Millennials have learned to seek information from the Internet whenever they need to know something and want to learn things. In this study, we present observations from several university courses with freely available online resources for the modern students. Ten different courses with video lectures were observed, often with positive outcomes and improved results compared to the previous course arrangements. Additionally, unlike in some previous literature, we observed that some issues such as the video length did not have a meaningful impact on the learning outcomes. Overall, the results indicate that videos offer excellent benefit-effort-ratio, and are an efficient way to reach the target audience: the students.
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Experimental Analysis of SPF Based Secure Web Application
Scientific article
In this paper we will propose model driven software development and Security Performance Framework (SPF) Model to maintain the balance between security and performance for web applications. We propose that all security in a Trusted Operating System is not necessary. Some non-essential security checks can be skipped to increase system performance. These non essential security checks can be identified in any web application. For implementation of this Security Performance framework based trusted operating system, we propose object oriented based Code generation through forward engineering. This involves generating source code of web application from one or more Object oriented Rational Rose model. The novel integration of security engineering with model-driven software expansion approach has varied advantages. To maintain security in various applications like Ecommerce, Banking, Marketplace services, Advertising, Auctions, Comparison shopping, Mobile commerce Payment, Ticketing, Online insurance policy management, we have to use high secured operating systems. In this regard a number of trusted operating systems like Argus, Trusted Solaris, and Virtual Vault have been developed by various companies to handle the increasing need of security. Due to high security reason these operating systems are being used in defense. But still these secure operating systems have limited scope in commercial sector due to lower performance; actually this security will come at a cost. This paper analyzes UML-based software development solutions for SPF to manage the security, performance and modeling for web applications.
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Explainable Fake News Detection Based on BERT and SHAP Applied to COVID-19
Scientific article
Fake news detection has become a significant research top in natural language processing. Since the outbreak of the covid-19 epidemic, a large amount of fake news about covid-19 has spread on social media, making the detection of fake news a challenging task. Applying deep learning models may improve predictions. However, their lack of explainability poses a challenge to their widespread adoption and use in practical applications. This work aims to design a deep learning framework for accurate and explainable prediction of covid-19 fake news. First, we choose BiLSTM as the base model and improve the classification performance of the BiLSTM model by incorporating BERT-based distillation. Then, a post-hoc interpretation method SHAP is used to explain the classification results of the model to improve the transparency of the model and increase people's confidence in the practical application. Finally, utilizing visual interpretation methods, such as significance plots, to analyze specific sample classification results for gaining insights into the key terms that influence the model’s decisions. Ablation experiments demonstrated the reliability of the explainable method.
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Explicit Instruction-based Methodology for Teaching Introductory Computer Programming
Scientific article
Non-computing students often encounter greater challenges in programming courses compared to their computing counterparts, primarily stemming from a lack of motivation in the subject. Motivation plays a pivotal role in the success of introductory programming (IP) modules, with intrinsically and extrinsically motivated students exhibiting greater enjoyment and engagement in learning activities. While numerous studies have attempted to enhance motivation in IP modules, most have focused on computing students which is influenced lar gely by the constructivist theory. This paper addresses this gap by proposing a cognitive-based teaching framework aimed at bolstering motivation among non-computing students. The proposed approach employs the Explicit Instruction paradigm, where the instructor first designs learning strategies and provides students with detailed explanations, demonstrations, examples, and non-examples. This enables the students to apply the strategies in groups, practice with feedback, and finally individually. The effectiveness of this approach was assessed using first-year students at two universities, one in South Africa and the other in Cameroon. We collected student motivation data using a quantitative questionnaire post-experiment. The results indicate that the proposed teaching method had a positive impact on participant motivation in terms of attendance, perceived relevance, confidence, and satisfaction. However, the specific degree of improvement varied among the participants.
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Scientific article
In the era of Artificial Intelligence (AI), where technology is transforming industries, education stands at a pivotal juncture. With an increasing emphasis on critical thinking and problem-solving, there is a growing need for innovative tools that can foster these essential skills among students. Traditional education methods need help making personalized scalable and interesting experiences for students at this task type which this research aims to solve. The research uses AI and deep learning tools to build an effective framework that enables better riddle solving for students by proposing state of the art deep features including sentence embeddings and ULMfit to be applied as input to deep learning models. In contrast, this study examines different traditional machine learning and deep learning models including ensemble learning models, used as baseline models for comparing the performance of the proposed transformer architectures based on RoBERTa-Large to determine which approach works best, achieving highest accuracy of 96% to effectively handle riddle complexity. The research studies used text data patterns using TF-IDF, Count Vectorization, and word embedding techniques which apply in the form of Roberta. Our research findings help educators, technology experts and scientific teams design educational tools with an easy-to-deploy AI solution.
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Exploring Holistic Well-Being: EDA and a Comparative ML Approach to Work-Life Balance
Scientific article
This research focuses on lifestyle and work-life balance, examining how different dynamics can influence holistic wellbeing, which focuses on the current literature to develop a framework that extends beyond standard well-being concepts. The work aims to enrich the discourse on holistic well-being by synthesizing existing knowledge and exploring new knowledge, providing valuable views for individuals and age groups involved in professionals striving to develop a more balanced and meaningful life. The study utilizes AI-driven methods and machine learning algorithms to analyse work-life indices to understand patterns and correlations that contribute or hinder work-life harmony. The findings highlight the importance of lifestyle choices, social connections, and personal fulfilment in achieving holistic wellness. The research provides evidence-based insights and practical recommendations to develop a healthy lifestyle and work-life balance. The study also examines the ethical implications of AI and highlights the need for a comprehensive approach to work-life balance. The study utilizes supervised learning algorithms and a comparative analysis of the accuracy scores of various algorithms, revealing significant differences in classification and more accuracy for the work-life balance score. The study aims to uncover insights beyond the typical contrasts and illuminate the interrelationship between numerous factors affecting an individual's well-being.
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Exploring Therapeutic Applications of Virtual Reality in Mental Health: A Bibliometric Analysis
Scientific article
This research explores the connection between interventions for mental well-being and Virtual Reality (VR). A bibliometric analysis was conducted to assess the state of the field, comparing mental health applications to understand VR's significance. The study revealed a limited but growing body of literature examining VR's effects on mental health, primarily targeting mood, stress, and anxiety disorders. Various approaches, including immersive VR experiences, were identified, offering unique therapeutic benefits. The comparative investigation across disorders underscored the potential of VR therapy to create synergistic effects when combined with other interventions. Immersive VR experiences were found to offer innovative ways to address emotional regulation and stress management, enhancing traditional therapeutic methods. The applications and techniques developed so far provide significant insights into the transformative role VR could play in mental health care. The findings emphasize the importance of further research to optimize and expand VR interventions for mental well-being. Such advancements could pave the way for more personalized, engaging, and effective mental health solutions, particularly for conditions resistant to conventional therapies. By leveraging VR’s immersive and interactive capabilities, mental health practitioners can create interventions that not only alleviate symptoms but also foster long-term psychological resilience. This study highlights the critical need to develop and implement VR-based interventions systematically, ensuring their accessibility and efficacy across diverse populations. By doing so, VR can serve as a cornerstone in the evolution of mental health care, bridging gaps and unlocking new possibilities for well-being.
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Scientific article
A group of researchers and developers from Colombia and Mexico have recognised that the development of state-of-the-art Extended Reality software, a key technology for the Metaverse, has great potential to improve teaching-learning processes in educational institutions. However, the development process does not take into account accessibility, universal design and inclusion, especially for the deaf student community. An extended reality model is proposed for the creation of this type of software as a tool to support access to knowledge, based on information gathering, requirements analysis, user-centred design and video game programming, including the ludic and didactic. The aim is to minimise the barriers that limit the learning of programming logic by students with hearing disabilities through the use of new technologies, creating spaces in virtual worlds that are understandable, usable and practical in conditions of safety, comfort and as much autonomy as possible. To validate the model, a mixed reality software prototype was designed and programmed to train students in programming logic, both deaf and hearing. User and heuristic tests were carried out, showing how immersion can improve knowledge acquisition processes and develop skills in higher education students.
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Extending the SOLO Model for Software-Based Projects
Scientific article
In the process of assessing learning outcomes, educators use constructive tools for evaluating students' understanding and performance. In the present study MIS students were engaged in a full life cycle project as part of a Software Analysis and Design workshop. For evaluating their performance, we used the SOLO (Structure of the Observed Learning Outcomes) taxonomy. However during the various stages of the workshop we encountered some inherent limitations of the taxonomy that led us to the understanding that the SOLO taxonomy should be enhanced. This paper elaborates on these missing but required enhancements.
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Extractive Text Summarization Using Modified Weighing and Sentence Symmetric Feature Methods
Scientific article
Text Summarization is a process that converts the original text into summarized form without changing the meaning of its contents. It finds its usefulness in many areas when the time to go through a large content is limited. This paper presents a comparative evaluation of statistical methods in extractive text summarization. Top score method is taken to be the bench mark for evaluation. Modified weighing method and modified sentence symmetric feature method are implemented with additional characteristic features to achieve a better performance than the benchmark method. Thematic weight and emphasize weights are added to conventional weighing method and the process of weight updation in sentence symmetric method is also modified in this paper. After evaluating these three methods using the standard measures, modified weighing method is identified as the best method with 80% efficiency.
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FPGA Based Pipelined Parallel Architecture for Fuzzy Logic Controller
Scientific article
This paper presents a high-speed VLSI fuzzy inference processor for the real-time applications using trapezoid-shaped membership functions. Analysis shows that the matching degree between two trapezoid-shaped membership functions can be obtained without traversing all the elements in the universal disclosure set of all possible conditions. A FPGA based pipelined parallel VLSI architecture has been proposed to take advantage of this basic idea, implemented on CycloneII-EP2C70F896C8. The controller is capable of processing fuzzified input. The proposed controller is designed for 2-input 1-output with maximum clock rate is 12.96 MHz and 275.33 MHz for 16 and 8 rules respectively. Thus, the inference speed is 0.81 and 34.41 MFLIPS for 16 and 8 rules, respectively.
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Face Recognition in Multi Camera Network with Sh Feature
Scientific article
Multi view face recognition using multiple camera networks is an active research area. The main aim of this paper is to handle different pose variations in multi camera network and recognizing face from those videos. The traditional approaches handle the pose estimation explicitly ,the proposed work will handle the multiple views of the poses .For a given set of multi view video sequences we use particle filter to track the 3D location of the head. The texture map is generated by back projecting the multi view video. The proposed work is developed using the Spherical Harmonic (SH) representation of the face from the texture mapped on to the sphere. A robust feature is constructed based on the properties of SH projection.
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Scientific article
The aim of this study is to investigate higher-education student's attitude toward the use of Facebook, as one of the social media tool, to enhance their education. This study was conducted on college students at one of the Jordanian higher educational institution. The number of participants of this study was forty five students who were enrolled in Computer Educational course. This course is either a graduation requirement for all elementary education major students or as an elective course for other student's non-computer majors. We noticed that each student has created a Facebook Page to use for the class purposes and other page(s) for their own personal needs. The result of the study showed that the Facebook site is a useful tool to deliver course materials and support learning. Students have showed their acceptance of this media to communicate with others and to find course materials and references. Most participants felt motivated and enjoyed going through the course when using the Facebook site.
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Factors Affecting Entrepreneurial Motivation and Intention of University Students in Hanoi, Vietnam
Scientific article
Entrepreneurship is the key driver of economic progress in many countries; thus, many countries have introduced policies to promote a more entrepreneurial environment. This study assesses the impact of factors affecting entrepreneurial intention of university students. The data was collected through a survey of 341 students at 09 leading universities in Hanoi, Vietnam and analyzed using structural equation modeling (SEM) with SPSS and Amos software. The research results show that entrepreneurial skills, entrepreneurial environment and subjective norms either directly or indirectly affect business motivation and entrepreneurial intention of university students. Thus, it is suggested that university and other educational institutions should provide more activities and taught courses that help students acquire the knowledge and skills necessary for entrepreneurship.
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