Pub Date : 2023-10-08DOI: 10.5815/ijmecs.2023.05.06
Ebenezer Eghan, Najim Ussiph, Obed Appiah
During COVID-19 pandemic, most tertiary institutions in Ghana were compelled to continue delivering of lectures online using internet technologies as was in the case of other countries. Senior high schools in Ghana were, however, not asked to do same, currently, the setting of most literature on blended or online learning in Ghana is focused on tertiary education. This paper situates the blended learning model in a less endowed senior high school to unearth the prospect of its implementation. The research provides an alternative to the traditional face-to-face learning, which is faced with the challenge of inadequate infrastructure, high number of students to class ratio, less compatibility with 21st learning skills and long-life learning in Ghana. A customed Moodle application as web application tool, hosted students online in both synchronous and asynchronous interactions. Purposive quota sampling size technique was used to select an appreciable sample size to fully go through the traditional face-face model for a term and then study through the blended learning model for another term. Students’ examination performances for both were analyzed with a paired t test statistical model. Interviews with participants were conducted to ascertain their evaluation of the blended learning model and questionnaires were also administered to discover the institutional, technological, and human resource readiness for blended learning in senior high schools. The analysis of the data gathered, proved that blended learning in senior high schools has high prospect and is better alternative to face-to-face learning in Ghana.
{"title":"Adoption of Blended Learning in Ghanaian Senior High Schools: A Case Study in a Less Endowed School","authors":"Ebenezer Eghan, Najim Ussiph, Obed Appiah","doi":"10.5815/ijmecs.2023.05.06","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.05.06","url":null,"abstract":"During COVID-19 pandemic, most tertiary institutions in Ghana were compelled to continue delivering of lectures online using internet technologies as was in the case of other countries. Senior high schools in Ghana were, however, not asked to do same, currently, the setting of most literature on blended or online learning in Ghana is focused on tertiary education. This paper situates the blended learning model in a less endowed senior high school to unearth the prospect of its implementation. The research provides an alternative to the traditional face-to-face learning, which is faced with the challenge of inadequate infrastructure, high number of students to class ratio, less compatibility with 21st learning skills and long-life learning in Ghana. A customed Moodle application as web application tool, hosted students online in both synchronous and asynchronous interactions. Purposive quota sampling size technique was used to select an appreciable sample size to fully go through the traditional face-face model for a term and then study through the blended learning model for another term. Students’ examination performances for both were analyzed with a paired t test statistical model. Interviews with participants were conducted to ascertain their evaluation of the blended learning model and questionnaires were also administered to discover the institutional, technological, and human resource readiness for blended learning in senior high schools. The analysis of the data gathered, proved that blended learning in senior high schools has high prospect and is better alternative to face-to-face learning in Ghana.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-08DOI: 10.5815/ijmecs.2023.05.04
Aya S. Noah, Naglaa E. Ghannam, Gaber A. Elsharawy, Abeer S. Desuky
There has been a significant rise in internet usage in recent years, which has led to the presence of data theft and the diversity of counterfeit materials. This has resulted the proliferation of cybercrimes and the theft of personal data via social media, e-mail, and phishing websites that are similar to the websites commonly used to grab user data details like that of a credit card or login ID. Phishing, a prevalent form of cybercrime, poses a danger to online security through the theft of personal information, and with the emergence of the COVID-19 virus, which has led to people and organizations being drawn towards the Internet and many people and companies being forced to work remotely, it has led to an increase in the existing phishing threats. Previously, hackers took advantage of the situation to infiltrate the devices of people and companies in numerous ways, which caused huge financial losses and damage to organizations. Based on previous results and research, Machine Learning (ML) is selected by researchers as an efficient method for identifying malicious software web pages from original web pages. This paper presents 30 characteristics of websites, which are analyzed using a correlation matrix to determine the relationship between variables. Feature selection is performed through a wrapper method and Extra Tree Classifiers (ETC) to identify the top-ranked characteristics (Features) for website classification. To evaluate web pages, various machine learning techniques such as Random Forest Tree (RF), Multilayer Perceptron (MLP), Decision Tree (DT), and Support Vector Machine (SVM) are used. The results of monitoring indicate that MLP, a deep neural network, outperforms all other techniques in terms of performance.
{"title":"An Intelligent System for Detecting Fake Materials on the Internet","authors":"Aya S. Noah, Naglaa E. Ghannam, Gaber A. Elsharawy, Abeer S. Desuky","doi":"10.5815/ijmecs.2023.05.04","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.05.04","url":null,"abstract":"There has been a significant rise in internet usage in recent years, which has led to the presence of data theft and the diversity of counterfeit materials. This has resulted the proliferation of cybercrimes and the theft of personal data via social media, e-mail, and phishing websites that are similar to the websites commonly used to grab user data details like that of a credit card or login ID. Phishing, a prevalent form of cybercrime, poses a danger to online security through the theft of personal information, and with the emergence of the COVID-19 virus, which has led to people and organizations being drawn towards the Internet and many people and companies being forced to work remotely, it has led to an increase in the existing phishing threats. Previously, hackers took advantage of the situation to infiltrate the devices of people and companies in numerous ways, which caused huge financial losses and damage to organizations. Based on previous results and research, Machine Learning (ML) is selected by researchers as an efficient method for identifying malicious software web pages from original web pages. This paper presents 30 characteristics of websites, which are analyzed using a correlation matrix to determine the relationship between variables. Feature selection is performed through a wrapper method and Extra Tree Classifiers (ETC) to identify the top-ranked characteristics (Features) for website classification. To evaluate web pages, various machine learning techniques such as Random Forest Tree (RF), Multilayer Perceptron (MLP), Decision Tree (DT), and Support Vector Machine (SVM) are used. The results of monitoring indicate that MLP, a deep neural network, outperforms all other techniques in terms of performance.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-08DOI: 10.5815/ijmecs.2023.05.01
Zamree Che-aron, Wannisa Matcha
With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.
{"title":"Project-Based Learning with Gallery Walk: The Association with the Learning Motivation and Achievement","authors":"Zamree Che-aron, Wannisa Matcha","doi":"10.5815/ijmecs.2023.05.01","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.05.01","url":null,"abstract":"With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-08DOI: 10.5815/ijmecs.2023.05.03
Ashritha R Murthy, Anil Kumar K M, Abdulbasit A. Darem
Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.
{"title":"Enhancing Emotion Detection with Adversarial Transfer Learning in Text Classification","authors":"Ashritha R Murthy, Anil Kumar K M, Abdulbasit A. Darem","doi":"10.5815/ijmecs.2023.05.03","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.05.03","url":null,"abstract":"Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-08DOI: 10.5815/ijmecs.2023.05.05
Tejaswita Garg, Sanjay K. Gupta
Digital footprints track online behaviors of an individual when communicating over social media platforms. In this paper, sentiment classification is carried out over online posts and tweets to pre detect whether a person is having neurological disorder or not. This study proposed a Hybrid Optimized Model Ensemble STACKed (HOMESTACK) algorithm built on stacked generalization approach that uses stacking and blending ensemble learning technique. The model is then evaluated over two datasets (Reddit Dataset1 & Twitter Dataset2) that include varied number of tweets. The pre-processing of the data and feature extraction is carried out to get cleaned text and vector corpus. The proposed HOMESTACK algorithm is then applied over training data using four base classifiers as Support Vector, Random Forest, K-Nearest Neighbor and CatBoost along with a Meta classifier as Logistic Regression. The testing data is then fed to the tuned model to compare the classification results and analysis. Also, Stacking and Blending ensemble frameworks and algorithms are proposed in this study. Execution time and metric evaluation are calculated in respect of Accuracy, Precision, Recall and F1-score. The experimental results clearly show that the proposed HOMESTACK algorithm performed better over chosen datasets as compared to blending ensemble and standalone machine learning classifiers.
{"title":"A Novel Algorithm for Stacked Generalization Approach to Predict Neurological Disorder over Digital Footprints","authors":"Tejaswita Garg, Sanjay K. Gupta","doi":"10.5815/ijmecs.2023.05.05","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.05.05","url":null,"abstract":"Digital footprints track online behaviors of an individual when communicating over social media platforms. In this paper, sentiment classification is carried out over online posts and tweets to pre detect whether a person is having neurological disorder or not. This study proposed a Hybrid Optimized Model Ensemble STACKed (HOMESTACK) algorithm built on stacked generalization approach that uses stacking and blending ensemble learning technique. The model is then evaluated over two datasets (Reddit Dataset1 & Twitter Dataset2) that include varied number of tweets. The pre-processing of the data and feature extraction is carried out to get cleaned text and vector corpus. The proposed HOMESTACK algorithm is then applied over training data using four base classifiers as Support Vector, Random Forest, K-Nearest Neighbor and CatBoost along with a Meta classifier as Logistic Regression. The testing data is then fed to the tuned model to compare the classification results and analysis. Also, Stacking and Blending ensemble frameworks and algorithms are proposed in this study. Execution time and metric evaluation are calculated in respect of Accuracy, Precision, Recall and F1-score. The experimental results clearly show that the proposed HOMESTACK algorithm performed better over chosen datasets as compared to blending ensemble and standalone machine learning classifiers.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.5815/ijmecs.2023.04.05
Zineb El Atmani, Mourad Madrane
: The need to change the professional practices in the nursing sciences obliges to accompany everyone towards a work on identity which engages a real process of professionalization. In addition, reflective practice (RP) has taken on more importance with this discourse on professionalization in nursing training settings. In this respect, reflection on practice is considered both as a competence of the professional teacher and upstream, in initial or continuing training, it is a tool for building one’s professional identity that can promote one’s professional development. From this perspective, this study aims to study the impact of reflexive practice on 235 teachers working at the level of 23 ISPITS (Higher Institute of Nursing Professions and Health Technologies) of the Kingdom of Morocco, by means of a questionnaire, whose internal validity has been approved, transmitted through the Google Forms platform. As such, this quantitative study will focus on two strands the 1st aims to study the existence of an impact of reflective practice on the professionalization of teaching within Moroccan ISPITS. The second objective is to study the nature of a possible relationship between the professionalization of teaching by the RP and the strengthening of the professional identity of ISPITS teachers. The results of this study show a positive impact of RP on the professionalization of nursing education; in addition, statistical tests have shown that there is a strong correlation between the professionalization of teaching in ISPITS and the strengthening of the professional identity of nursing teachers. Also, this study could contribute to improving other vocational training. In addition, consideration of occupational identity, as well as the relationship between it and its interactive experience with others and in varied environments that elicit reflective feedback on its professional practice, are likely to promote the professional development of each practitioner. The teacher's use of reflexivity is inseparable from his identity work. It will undoubtedly lead to the professionalization of training. In this perspective, similar studies can be carried out to deepen this theme and enhance their interventions.
{"title":"Reflective Practice as a way of developing the professional identity of Teachers and Professionalizing Nursing Education","authors":"Zineb El Atmani, Mourad Madrane","doi":"10.5815/ijmecs.2023.04.05","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.04.05","url":null,"abstract":": The need to change the professional practices in the nursing sciences obliges to accompany everyone towards a work on identity which engages a real process of professionalization. In addition, reflective practice (RP) has taken on more importance with this discourse on professionalization in nursing training settings. In this respect, reflection on practice is considered both as a competence of the professional teacher and upstream, in initial or continuing training, it is a tool for building one’s professional identity that can promote one’s professional development. From this perspective, this study aims to study the impact of reflexive practice on 235 teachers working at the level of 23 ISPITS (Higher Institute of Nursing Professions and Health Technologies) of the Kingdom of Morocco, by means of a questionnaire, whose internal validity has been approved, transmitted through the Google Forms platform. As such, this quantitative study will focus on two strands the 1st aims to study the existence of an impact of reflective practice on the professionalization of teaching within Moroccan ISPITS. The second objective is to study the nature of a possible relationship between the professionalization of teaching by the RP and the strengthening of the professional identity of ISPITS teachers. The results of this study show a positive impact of RP on the professionalization of nursing education; in addition, statistical tests have shown that there is a strong correlation between the professionalization of teaching in ISPITS and the strengthening of the professional identity of nursing teachers. Also, this study could contribute to improving other vocational training. In addition, consideration of occupational identity, as well as the relationship between it and its interactive experience with others and in varied environments that elicit reflective feedback on its professional practice, are likely to promote the professional development of each practitioner. The teacher's use of reflexivity is inseparable from his identity work. It will undoubtedly lead to the professionalization of training. In this perspective, similar studies can be carried out to deepen this theme and enhance their interventions.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41484825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.5815/ijmecs.2023.04.06
Dmytro Uhryn, Yuriy Ushenko, V. Lytvyn, Zhengbing Hu, O. Lozynska, V. Ilin, Artur Hostiuk
,
，
{"title":"Modelling of an Intelligent Geographic Information System for Population Migration Forecasting","authors":"Dmytro Uhryn, Yuriy Ushenko, V. Lytvyn, Zhengbing Hu, O. Lozynska, V. Ilin, Artur Hostiuk","doi":"10.5815/ijmecs.2023.04.06","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.04.06","url":null,"abstract":",","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41373931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.5815/ijmecs.2023.04.01
M. Segura, Ramiro Osorio, Adriana Zavala
: 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.
{"title":"Extended Reality Model for Accessibility in Learning for Deaf and Hearing Students (Programming Logic Case)","authors":"M. Segura, Ramiro Osorio, Adriana Zavala","doi":"10.5815/ijmecs.2023.04.01","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.04.01","url":null,"abstract":": 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.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48529277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.5815/ijmecs.2023.04.03
S. Barde, Sangeeta Tiwari, Brijesh Patel
: Astrology is a very ancient and traditional method of prediction that increases the interest of people continuously. The globe today, there are no common guidelines or principles for astrological prediction. Rather than setting universal principles and criteria for astrological prediction, astrologers focus on providing high-quality services to individuals but there is no guarantee of accuracy. Machine learning is providing the best result for analysis and prediction on many applications by the learning of computers. Prediction and classification make it possible for any learner to work on large, noisy, and complex datasets. The main motive of the paper is to introduce a scientific approach that reduces the drawback of the traditional approach and indicates the universal rules of prediction and proves the validity of astrology by the three classification techniques, Naïve Bayes, Logistic-R, and J48. It is a part of supervision learning that operates with cross-validation 10,12, and 14fold for calculating the terms 1) correctly classified instances (CCI), erroneously categorized instances (ECI), Mean absolute error (MAE), Root mean squared error (RMSE), and Relative absolute error (RAE). 2) True Positive Rate, False Positive Rate, Precision, and F-Measure values. 3) The MCC, ROC, and PRC area values. 4) To calculate the average weight of the three-class label professor, businessman, and doctor in terms of true positive rate, false-positive rate, precision, F-measure, PRC, and ROC area, 5) finally, we calculated the accuracy of each classification technique and compare which provide the better result. For this, we have collected the date of birth, place of birth, and time of birth of 100 persons who belong to different professions. 40 data of professors, 30 data of businessmen, and 30 data of doctors, prepare the horoscope of an individual with the help of software. For analysis, we create the datasheet in .csv format and apply this data sheet in the weka tool to check various parameters and the accuracy percentage of each classifier.
{"title":"Scientific Approach of Prediction for Professions Using Machine Learning Classification Techniques","authors":"S. Barde, Sangeeta Tiwari, Brijesh Patel","doi":"10.5815/ijmecs.2023.04.03","DOIUrl":"https://doi.org/10.5815/ijmecs.2023.04.03","url":null,"abstract":": Astrology is a very ancient and traditional method of prediction that increases the interest of people continuously. The globe today, there are no common guidelines or principles for astrological prediction. Rather than setting universal principles and criteria for astrological prediction, astrologers focus on providing high-quality services to individuals but there is no guarantee of accuracy. Machine learning is providing the best result for analysis and prediction on many applications by the learning of computers. Prediction and classification make it possible for any learner to work on large, noisy, and complex datasets. The main motive of the paper is to introduce a scientific approach that reduces the drawback of the traditional approach and indicates the universal rules of prediction and proves the validity of astrology by the three classification techniques, Naïve Bayes, Logistic-R, and J48. It is a part of supervision learning that operates with cross-validation 10,12, and 14fold for calculating the terms 1) correctly classified instances (CCI), erroneously categorized instances (ECI), Mean absolute error (MAE), Root mean squared error (RMSE), and Relative absolute error (RAE). 2) True Positive Rate, False Positive Rate, Precision, and F-Measure values. 3) The MCC, ROC, and PRC area values. 4) To calculate the average weight of the three-class label professor, businessman, and doctor in terms of true positive rate, false-positive rate, precision, F-measure, PRC, and ROC area, 5) finally, we calculated the accuracy of each classification technique and compare which provide the better result. For this, we have collected the date of birth, place of birth, and time of birth of 100 persons who belong to different professions. 40 data of professors, 30 data of businessmen, and 30 data of doctors, prepare the horoscope of an individual with the help of software. For analysis, we create the datasheet in .csv format and apply this data sheet in the weka tool to check various parameters and the accuracy percentage of each classifier.","PeriodicalId":36486,"journal":{"name":"International Journal of Modern Education and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43433339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08DOI: 10.5815/ijmecs.2023.04.04
Selamat Triono Ahmad, Ronal Watrianthos, Agariadne Dwinggo Samala, M. Muskhir, G. Dogara
: The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.
基于项目的学习(PjBL)模式通常被认为是职业教育中最先进的模式。PjBL范式在职业教育中越来越多的应用是一个有趣的研究课题。随着信息技术的快速发展，PjBL在职业教育中能够帮助学生培养解决问题、批判性思维和团队合作能力。本研究采用文献计量学方法，深入探讨职业教育项目学习的学科结构、社会网络、研究趋势和问题。在2022年11月27日，使用标题中的基于项目的学习术语搜索Scopus数据库。第二个搜索字段出现在标题、摘要和关键词职业教育或TVET中，仅限于期刊文章或会议记录，并且是英文的，以保持最新的。该分析揭示了2010年至2022年间scopus索引期刊和论文集中的60篇文章。来自印度尼西亚玛琅州立大学的Dwi Agus Sudjimat是最多产的作者，他撰写了四篇关于这个主题的文章。印度尼西亚是在开发PjBL模型方面投资最多的国家。从专题数据来看，基于项目的学习位于第一象限，具有较高的中心性和密度，并且与学习主题相关的问题较为发达。本研究的结果表明，基于项目的学习模式在职业教育中不断发展，可能会继续成为这一领域的重要教学方法。
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