Pub Date : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031783
S. Shiaeles
One of the most important security issues is unauthorised access to computer systems. The number of leaked passwords and credentials grows exponentially each year, showing that current protection systems and authentication methods are insufficient. Attackers are bypassing the state-of-the-art systems and gaining access to corporate environments as well as our personal accounts comprising confidentiality and threatening privacy. This work focuses on a new password authentication scheme utilising 3D graphical passwords and Hyper Ledger Fabric. The initial implementation shows that this method is promising and can offer users and organisations better security minimising the risk of stolen credentials.
{"title":"GRABLOK: A Novel Graphical Password Authentication Utilising Blockchain Technology","authors":"S. Shiaeles","doi":"10.1109/ICOCO56118.2022.10031783","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031783","url":null,"abstract":"One of the most important security issues is unauthorised access to computer systems. The number of leaked passwords and credentials grows exponentially each year, showing that current protection systems and authentication methods are insufficient. Attackers are bypassing the state-of-the-art systems and gaining access to corporate environments as well as our personal accounts comprising confidentiality and threatening privacy. This work focuses on a new password authentication scheme utilising 3D graphical passwords and Hyper Ledger Fabric. The initial implementation shows that this method is promising and can offer users and organisations better security minimising the risk of stolen credentials.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117091825","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031739
Muhammad Badrisya Nordin, S. B. Hisham
This project aims to help farmers in Lumut, Perak to combat thrips invasion on mango trees. It would help reduce loss of fruit-producing branches, manual inspections, and the need to cover large acres of land manually. Data was collected by using a Canon DSLR camera at lm distance in natural lighting and uncontrolled background. Images of healthy and diseased new leaves are pre-processed to remove noise. Masking and thresholding using a range of intensity values are used to remove the background. After that, the images were clustered using Fuzzy C-Means clustering. It was found that this method was more suitable than K-Means clustering as it uses a soft clustering approach. The images obtained were then classified using Support Vector Machine (SVM). An average classification accuracy of 9S.52% was achieved.
{"title":"Detection of Insect Invasion Symptoms on Tree Leaves Using Image Processing","authors":"Muhammad Badrisya Nordin, S. B. Hisham","doi":"10.1109/ICOCO56118.2022.10031739","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031739","url":null,"abstract":"This project aims to help farmers in Lumut, Perak to combat thrips invasion on mango trees. It would help reduce loss of fruit-producing branches, manual inspections, and the need to cover large acres of land manually. Data was collected by using a Canon DSLR camera at lm distance in natural lighting and uncontrolled background. Images of healthy and diseased new leaves are pre-processed to remove noise. Masking and thresholding using a range of intensity values are used to remove the background. After that, the images were clustered using Fuzzy C-Means clustering. It was found that this method was more suitable than K-Means clustering as it uses a soft clustering approach. The images obtained were then classified using Support Vector Machine (SVM). An average classification accuracy of 9S.52% was achieved.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547382","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031970
Nur Amira Mirza Nazar, Puteri Nurul Shahira Sabki, N. Ibrahim, Siti Afiqah Muhamad Jamil, Mahayaudin M. Mansor
The indicator of bankruptcy exposure for airport operations in Malaysia is calculated by using Altman’s Z”-score. Financial and non-financial attributes related to the bankruptcy exposure show multicollinearity, and the redundant information was identified and removed. The common period for the variables is from 1999-2021, which includes the period of COVID-19 pandemic. Models with a combination of financial and non-financial attributes further reduce the deviation between the estimated standard deviation of the residuals and the marginal standard deviation of the bankruptcy risk in comparison to models without the combination. The best model provides improvements in terms of the mean of the absolute errors (MAE), mean of absolute percentage errors (MAPE), and mean absolute scaled errors (MASE). Furthermore, all determinants in the best model are statistically significant. We suggest that the opportunity for optimisation, including total movements of passenger, cargo and mail, could reduce the company’s bankruptcy exposure. Findings indicate that reducing the financial leverage could improve the financial distress risk while liquidity, net operating margin, and asset turnover are positively contributed to the financial stability of the largest airport operator in Malaysia. If the marginal average of annual exposures to bankruptcy of 4.04% continues linearly into the future, the company is expected to transition from being financially stable to experiencing financial distress in 2030.
{"title":"Measuring Evidence Affecting the Financial Stability of Airport Operations in Malaysia","authors":"Nur Amira Mirza Nazar, Puteri Nurul Shahira Sabki, N. Ibrahim, Siti Afiqah Muhamad Jamil, Mahayaudin M. Mansor","doi":"10.1109/ICOCO56118.2022.10031970","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031970","url":null,"abstract":"The indicator of bankruptcy exposure for airport operations in Malaysia is calculated by using Altman’s Z”-score. Financial and non-financial attributes related to the bankruptcy exposure show multicollinearity, and the redundant information was identified and removed. The common period for the variables is from 1999-2021, which includes the period of COVID-19 pandemic. Models with a combination of financial and non-financial attributes further reduce the deviation between the estimated standard deviation of the residuals and the marginal standard deviation of the bankruptcy risk in comparison to models without the combination. The best model provides improvements in terms of the mean of the absolute errors (MAE), mean of absolute percentage errors (MAPE), and mean absolute scaled errors (MASE). Furthermore, all determinants in the best model are statistically significant. We suggest that the opportunity for optimisation, including total movements of passenger, cargo and mail, could reduce the company’s bankruptcy exposure. Findings indicate that reducing the financial leverage could improve the financial distress risk while liquidity, net operating margin, and asset turnover are positively contributed to the financial stability of the largest airport operator in Malaysia. If the marginal average of annual exposures to bankruptcy of 4.04% continues linearly into the future, the company is expected to transition from being financially stable to experiencing financial distress in 2030.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123978567","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031771
Muhammad Syafiq Nordin, A. L. Asnawi, Nur Aishah Binti Zainal, R. F. Olanrewaju, A. Jusoh, S. Ibrahim, N. F. M. Azmin
The effect of stress on mental and physical health is very concerning making it a fascinating and socially valuable field of study nowadays. Although a number of stress markers have been deployed, there are still issues involved with using these kinds of approaches. By developing a speech-based stress detection system, it could solve the problems faced by other currently available methods of detecting stress since it is a non-invasive and contactless approach. In this work, a fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC) namely Teager-MFCC (T-MFCC) are proposed as the speech features to be extracted from speech signals in recognizing stressed emotions. Since stressed emotions affect the nonlinear components of speech, TEO is applied to reflect the instantaneous energy of the components. Convolutional Neural Network (CNN) classifier is used with the proposed T- MFCC features on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) corpus. The proposed method (T-MFCC) had shown a better performance with classification accuracies of 95.83% and 95.37% for male and female speakers respectively compared to the MFCC feature extraction technique which achieves 84.26% (male) and 93.98% (female) classification accuracies.
{"title":"Stress Detection based on TEO and MFCC speech features using Convolutional Neural Networks (CNN)","authors":"Muhammad Syafiq Nordin, A. L. Asnawi, Nur Aishah Binti Zainal, R. F. Olanrewaju, A. Jusoh, S. Ibrahim, N. F. M. Azmin","doi":"10.1109/ICOCO56118.2022.10031771","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031771","url":null,"abstract":"The effect of stress on mental and physical health is very concerning making it a fascinating and socially valuable field of study nowadays. Although a number of stress markers have been deployed, there are still issues involved with using these kinds of approaches. By developing a speech-based stress detection system, it could solve the problems faced by other currently available methods of detecting stress since it is a non-invasive and contactless approach. In this work, a fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC) namely Teager-MFCC (T-MFCC) are proposed as the speech features to be extracted from speech signals in recognizing stressed emotions. Since stressed emotions affect the nonlinear components of speech, TEO is applied to reflect the instantaneous energy of the components. Convolutional Neural Network (CNN) classifier is used with the proposed T- MFCC features on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) corpus. The proposed method (T-MFCC) had shown a better performance with classification accuracies of 95.83% and 95.37% for male and female speakers respectively compared to the MFCC feature extraction technique which achieves 84.26% (male) and 93.98% (female) classification accuracies.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127728693","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031677
Mohammad Raihan Goni, Nur Intan Raihana Ruhaiyem
The brain tumor is recognized as one of the most frequent tumors, with a significant mortality rate associated with its development. Segmentation of brain tumors involves distinguishing normal brain tissue from malignant tissue. When evaluating brain tumors, it is possible to determine the existence of tumor tissue quickly. However, accurate and reproducible segmentation and characterization of anomalies are not readily achievable. Consequently, several researchers have proposed various biomedical image segmentation methods to distinguish between tumor and normal brain tissue reliably. However, state-of-the-art segmentation has not been achieved by the existing brain tumor segmentation models, and they often come with high model complexity. Att-Sharp-U-net, a model influenced by the actual U-net model utilized in various medical image segmentation research, is presented as a contribution by this study. Two critical alterations to the underlying U-net model have been incorporated into the model: a grid-based attention block and a sharp block. By doing this, we were able to address the constraints of the U-net model while simultaneously enhancing segmentation performance with increasing negligible computational complexity. Experiments on the Brats2020 dataset, a recent publicly available benchmark dataset in brain tumor segmentation, showed that the proposed model improved segmentation performance with a dice score of 0.9275 and Jaccard score of 0.8684 when compared to the baselines.
{"title":"Salient feature extraction using Attention for Brain Tumor segmentation","authors":"Mohammad Raihan Goni, Nur Intan Raihana Ruhaiyem","doi":"10.1109/ICOCO56118.2022.10031677","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031677","url":null,"abstract":"The brain tumor is recognized as one of the most frequent tumors, with a significant mortality rate associated with its development. Segmentation of brain tumors involves distinguishing normal brain tissue from malignant tissue. When evaluating brain tumors, it is possible to determine the existence of tumor tissue quickly. However, accurate and reproducible segmentation and characterization of anomalies are not readily achievable. Consequently, several researchers have proposed various biomedical image segmentation methods to distinguish between tumor and normal brain tissue reliably. However, state-of-the-art segmentation has not been achieved by the existing brain tumor segmentation models, and they often come with high model complexity. Att-Sharp-U-net, a model influenced by the actual U-net model utilized in various medical image segmentation research, is presented as a contribution by this study. Two critical alterations to the underlying U-net model have been incorporated into the model: a grid-based attention block and a sharp block. By doing this, we were able to address the constraints of the U-net model while simultaneously enhancing segmentation performance with increasing negligible computational complexity. Experiments on the Brats2020 dataset, a recent publicly available benchmark dataset in brain tumor segmentation, showed that the proposed model improved segmentation performance with a dice score of 0.9275 and Jaccard score of 0.8684 when compared to the baselines.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114410928","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031718
E. Piedad, Elmer C. Peramo, Jeffrey A. Aborot, Joshua Russel Bensig, Paulyn Jamila Deiparine, Stephanie Marie Flores, Ciara Gumera, Franz A de Leon
An intelligent flood detection system is developed from an existing traffic surveillance structure. Images are captured from closed-circuit television (CCTV) with actual setting conditions - (a) normal, raining and flooding, and (b) day and night. The proposed system applied scene parsing method to avoid the impact of varying the physical setting of CCTV structures. This image parsing method uses pre-trained model, DeepLabv3, to detect objects common to traffic CCTV images such as road and vehicles. Supervised learning is performed to detect floods based on a convolutional neural network (CNN) model. The CNN model is validated ten times by training and testing it with randomly partitioned training and testing datasets, respectively. Initial results show that all validating models perform very close to each other. The best-trained model yields 80.67% accuracy, 86.33% precision, 81% recall, and 79.67% F1-score which shows satisfactory performance. This initial system brings the first step to a more reliable flood monitoring system.
{"title":"Intelligent Flood Detection using Traffic Surveillance Images based on Convolutional Neural Network and Image Parsing","authors":"E. Piedad, Elmer C. Peramo, Jeffrey A. Aborot, Joshua Russel Bensig, Paulyn Jamila Deiparine, Stephanie Marie Flores, Ciara Gumera, Franz A de Leon","doi":"10.1109/ICOCO56118.2022.10031718","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031718","url":null,"abstract":"An intelligent flood detection system is developed from an existing traffic surveillance structure. Images are captured from closed-circuit television (CCTV) with actual setting conditions - (a) normal, raining and flooding, and (b) day and night. The proposed system applied scene parsing method to avoid the impact of varying the physical setting of CCTV structures. This image parsing method uses pre-trained model, DeepLabv3, to detect objects common to traffic CCTV images such as road and vehicles. Supervised learning is performed to detect floods based on a convolutional neural network (CNN) model. The CNN model is validated ten times by training and testing it with randomly partitioned training and testing datasets, respectively. Initial results show that all validating models perform very close to each other. The best-trained model yields 80.67% accuracy, 86.33% precision, 81% recall, and 79.67% F1-score which shows satisfactory performance. This initial system brings the first step to a more reliable flood monitoring system.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124731915","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031671
Afiqah Aqilah Adzhar, Zulaile Mabni, Z. Ibrahim
Phishing Email can be described as an email that looks exactly like a legitimate email, but it is designed by phisher with an intention to deceive the email’s user. The purpose of phishing email is to trick email user to visit fake website that looks exactly like a real one or to trick user to download the available attachment in the email without knowing that they are downloading virus into their machine. As the number of phishing emails are increasing from day to day and due to the complexity in detecting phishing email, there are numbers of continuous researches that have been done to improve existing detection tools or to develop a new one. To provide a thorough understanding of phishing attacks, this paper provides a brief explanation on phishing email and phishing attack. This paper presents the comparison of previous studies in commonly used Supervised Machine Learning techniques on detecting the phishing email attack such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector machine(SVM). The findings of this study concluded that SVM and RF are the best techniques that can be used to detect phishing email.
{"title":"A Comparative Study on Email Phishing Detection Using Machine Learning Techniques","authors":"Afiqah Aqilah Adzhar, Zulaile Mabni, Z. Ibrahim","doi":"10.1109/ICOCO56118.2022.10031671","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031671","url":null,"abstract":"Phishing Email can be described as an email that looks exactly like a legitimate email, but it is designed by phisher with an intention to deceive the email’s user. The purpose of phishing email is to trick email user to visit fake website that looks exactly like a real one or to trick user to download the available attachment in the email without knowing that they are downloading virus into their machine. As the number of phishing emails are increasing from day to day and due to the complexity in detecting phishing email, there are numbers of continuous researches that have been done to improve existing detection tools or to develop a new one. To provide a thorough understanding of phishing attacks, this paper provides a brief explanation on phishing email and phishing attack. This paper presents the comparison of previous studies in commonly used Supervised Machine Learning techniques on detecting the phishing email attack such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector machine(SVM). The findings of this study concluded that SVM and RF are the best techniques that can be used to detect phishing email.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129709550","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031805
Sabariha Che Hussin, S. N. Kew, Z. Tasir, Liew Tze Wei, Tran Tich Phuoc
Feedback is a significant part of learning system and framework. Since instructors and students are physically separated in online platform, feedback becomes compulsory tools to be implemented in assisting the process of teaching and learning. Therefore, this systematic review paper aims to find out which forms of English communication skills being focussed on online learning, to explore the varieties of feedback provision that are used to enhance English communication skills in online learning and lastly to find out if the feedback implementation in online learning has affected students positively, especially in English language. Despite the fact that feedback plays a significant role in assisting students, there have been few studies that examine the progress made so far as reported in the literature, and which type of feedback has actually substantial in improving and enhancing the learners’ communication skills especially in English language. Varieties of feedback discovered; and all intended to aid and enhance the communication skills in online learning, especially in English language. When conducting this systematic literature review study, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommended procedures were used, and a total of 30 articles were found by utilising online databases such as Scopus, IEEEXplore Digital Library and etc, as according to the review selection guideline. Feedback provision, communication skills, online learning, student, and English language were employed as search keywords. The results were analysed in light of the three focuses described. Interestingly, it is revealed that the feedback utilization has positively affecting the students especially in English language learning via online learning.
反馈是学习系统和框架的重要组成部分。由于教师和学生在网络平台上的物理分离，反馈成为辅助教学过程的强制性工具。因此，这篇系统综述论文旨在找出在线学习中关注的是哪些形式的英语沟通技巧，探索在线学习中用于提高英语沟通技巧的反馈提供的种类，最后发现在线学习中反馈的实施是否对学生产生了积极的影响，特别是在英语语言方面。尽管反馈在帮助学生方面起着重要的作用，但很少有研究调查迄今为止文献中所报道的进展，以及哪种类型的反馈实际上在改善和增强学习者的沟通技巧(尤其是英语)方面具有实质性的作用。发现的各种反馈;所有这些都是为了帮助和提高在线学习的沟通技巧，尤其是英语学习。在进行系统文献综述研究时，采用了PRISMA (Preferred Reporting Items for systematic Reviews and meta - analysis)推荐的程序，根据综述选择指南，利用Scopus、IEEEXplore Digital Library等在线数据库共发现了30篇文章。搜索关键词为反馈提供、沟通技巧、在线学习、学生和英语。根据所描述的三个重点对结果进行了分析。有趣的是，研究发现反馈的使用对学生的英语学习产生了积极的影响，尤其是在在线英语学习中。
{"title":"A Systematic Review: Types of Feedback Provision in Enhancing English Language in Online Learning Environment","authors":"Sabariha Che Hussin, S. N. Kew, Z. Tasir, Liew Tze Wei, Tran Tich Phuoc","doi":"10.1109/ICOCO56118.2022.10031805","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031805","url":null,"abstract":"Feedback is a significant part of learning system and framework. Since instructors and students are physically separated in online platform, feedback becomes compulsory tools to be implemented in assisting the process of teaching and learning. Therefore, this systematic review paper aims to find out which forms of English communication skills being focussed on online learning, to explore the varieties of feedback provision that are used to enhance English communication skills in online learning and lastly to find out if the feedback implementation in online learning has affected students positively, especially in English language. Despite the fact that feedback plays a significant role in assisting students, there have been few studies that examine the progress made so far as reported in the literature, and which type of feedback has actually substantial in improving and enhancing the learners’ communication skills especially in English language. Varieties of feedback discovered; and all intended to aid and enhance the communication skills in online learning, especially in English language. When conducting this systematic literature review study, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommended procedures were used, and a total of 30 articles were found by utilising online databases such as Scopus, IEEEXplore Digital Library and etc, as according to the review selection guideline. Feedback provision, communication skills, online learning, student, and English language were employed as search keywords. The results were analysed in light of the three focuses described. Interestingly, it is revealed that the feedback utilization has positively affecting the students especially in English language learning via online learning.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129598929","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 : 2022-11-14DOI: 10.1109/ICOCO56118.2022.10031633
Hong Yi Khaw, Phei-Chin Lim, S. K. Jali
Mandarin is difficult for a couple of reasons, such as the complexity of writing system, Chinese characters, and tone. It can be excruciatingly hard to learn Mandarin without motivation and immediate positive feedback. With the growth of technology, games now play an essential role in language learning. Games enable learners to actively participate in activities, and to strengthen their affective reactions such as interest and motivation. There are various language learning games available on the market but most of them are using similar ways such as flashcards which is very repetitive. A first-person view gameplay will be developed in this project to explore the possibility of immersive game-based learning and to provide an entertaining learning environment that motivates learners. The knowledge of vocabulary words for three topics which are numbers, colours, and direction are covered. Players are recruited to participate in our experiments. A paired sample t-test, t(14)=27.4,p<.001 showed that there is improvement in the Mandarin learning achievement of players before and after playing the game. The average mean value of 4.55 is achieved using the RIMMS survey showed promising result in perceived motivation of the tested gameplay.
{"title":"Do You Know My Name? Learning Mandarin through Game-based Learning","authors":"Hong Yi Khaw, Phei-Chin Lim, S. K. Jali","doi":"10.1109/ICOCO56118.2022.10031633","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031633","url":null,"abstract":"Mandarin is difficult for a couple of reasons, such as the complexity of writing system, Chinese characters, and tone. It can be excruciatingly hard to learn Mandarin without motivation and immediate positive feedback. With the growth of technology, games now play an essential role in language learning. Games enable learners to actively participate in activities, and to strengthen their affective reactions such as interest and motivation. There are various language learning games available on the market but most of them are using similar ways such as flashcards which is very repetitive. A first-person view gameplay will be developed in this project to explore the possibility of immersive game-based learning and to provide an entertaining learning environment that motivates learners. The knowledge of vocabulary words for three topics which are numbers, colours, and direction are covered. Players are recruited to participate in our experiments. A paired sample t-test, t(14)=27.4,p<.001 showed that there is improvement in the Mandarin learning achievement of players before and after playing the game. The average mean value of 4.55 is achieved using the RIMMS survey showed promising result in perceived motivation of the tested gameplay.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127110433","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}
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