The purpose of this qualitative research is to investigate the perceptions of Flemish and Thai pre-service teachers and university supervisors on e-coaching during their internship programs. Data was obtained by conducting interviews and with in-depth semi-structured questions with N=40 participants. The instrument was based on an integrated model of the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM). The interviews were audio-recorded and encrypted in NVivo 11. The results revealed that both Flemish and Thai participants perceived the value of e-coaching. The subjective norms of the participants show that Thai students and supervisors influence each other to use e-coaching. For Flemish, the supervisors have an essential role in influencing students. Both Thai and Flemish perceived the use of e-coaching as a comfortable mode of communication. The results explained by the theoretical framework of Planned Behavior and Technology Acceptance (TPB -TAM) variables also show some differences between the two contexts. The Thai participants are positive about the use of e-coaching because of its ease of use, and have intention to increase the use in the future; however, Flemish participants are skeptical towards this future. In general, Thai participants believe that using Information Communication Technology (ICT) tools can enrich their coaching process, while Flemish participants feel uncertain. Nevertheless, almost all participants agree that e-coaching is crucial for an effective coaching but should always be complemented to a face-to-face coaching.
{"title":"E-coaching for Pre-service Teacher Internship: Thais and Flemish' Perceptions","authors":"J. Nasongkhla","doi":"10.1145/3345094.3345100","DOIUrl":"https://doi.org/10.1145/3345094.3345100","url":null,"abstract":"The purpose of this qualitative research is to investigate the perceptions of Flemish and Thai pre-service teachers and university supervisors on e-coaching during their internship programs. Data was obtained by conducting interviews and with in-depth semi-structured questions with N=40 participants. The instrument was based on an integrated model of the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM). The interviews were audio-recorded and encrypted in NVivo 11. The results revealed that both Flemish and Thai participants perceived the value of e-coaching. The subjective norms of the participants show that Thai students and supervisors influence each other to use e-coaching. For Flemish, the supervisors have an essential role in influencing students. Both Thai and Flemish perceived the use of e-coaching as a comfortable mode of communication. The results explained by the theoretical framework of Planned Behavior and Technology Acceptance (TPB -TAM) variables also show some differences between the two contexts. The Thai participants are positive about the use of e-coaching because of its ease of use, and have intention to increase the use in the future; however, Flemish participants are skeptical towards this future. In general, Thai participants believe that using Information Communication Technology (ICT) tools can enrich their coaching process, while Flemish participants feel uncertain. Nevertheless, almost all participants agree that e-coaching is crucial for an effective coaching but should always be complemented to a face-to-face coaching.","PeriodicalId":160662,"journal":{"name":"Proceedings of the 4th International Conference on Information and Education Innovations","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122031314","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}
Measuring how a college is successful relies heavily on its outcome (i.e., students of the institution). After spending a few years in a college, students will join organizations where they can apply knowledge and skills acquired during the study-life. Therefore, it is vital to ensure that students are well treated, and to achieve that we need to understand how to improve the education environment. To improve an education environment, we need to learn that from factors that impact on success or failure. Data mining studies in education can be descriptive, predictive, and explanatory (i.e., diagnostic). Although Predictive models can tell what would very likely to happen when certain factors are present, they cannot tell how these were occurred. Therefore, explanatory models can explain how underlying factors are exist and can quantify their level existence which will lead to improving education practice in general. Underlying factors include independent variables (e.g., gender, age, disability) and the interaction between these variables. In this paper, we define potential methods that can help to provide explanatory studies using educational data. Also, we define machine learning algorithms (i.e., regression tools) that can be used for this type of study including preprocessing the data, test of multicollinearity of the specified model, interactions involvement, and model validation. In addition, we presented a case study using synthetic data to explain how this method is implemented. In the case study, we explained variables and interactions contributed to students scores. Also, we reported performance measures used for the linear outcome.
{"title":"Applying explanatory analysis in education using different regression methods","authors":"Y. Alshehri","doi":"10.1145/3345094.3345111","DOIUrl":"https://doi.org/10.1145/3345094.3345111","url":null,"abstract":"Measuring how a college is successful relies heavily on its outcome (i.e., students of the institution). After spending a few years in a college, students will join organizations where they can apply knowledge and skills acquired during the study-life. Therefore, it is vital to ensure that students are well treated, and to achieve that we need to understand how to improve the education environment. To improve an education environment, we need to learn that from factors that impact on success or failure. Data mining studies in education can be descriptive, predictive, and explanatory (i.e., diagnostic). Although Predictive models can tell what would very likely to happen when certain factors are present, they cannot tell how these were occurred. Therefore, explanatory models can explain how underlying factors are exist and can quantify their level existence which will lead to improving education practice in general. Underlying factors include independent variables (e.g., gender, age, disability) and the interaction between these variables. In this paper, we define potential methods that can help to provide explanatory studies using educational data. Also, we define machine learning algorithms (i.e., regression tools) that can be used for this type of study including preprocessing the data, test of multicollinearity of the specified model, interactions involvement, and model validation. In addition, we presented a case study using synthetic data to explain how this method is implemented. In the case study, we explained variables and interactions contributed to students scores. Also, we reported performance measures used for the linear outcome.","PeriodicalId":160662,"journal":{"name":"Proceedings of the 4th International Conference on Information and Education Innovations","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115315402","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}
This paper evaluated whether the learning environment can affect students' performance in reading, mathematics and science. Using the data from PISA, the paper analyzed the relationship between having classic literature, books of poetry, and works of art and students' scores in reading, mathematics and science using Hotelling's T-squared test and three-way between-subjects MANOVA.
{"title":"Research on the Effects of Learning environment on Students' Academic Performance","authors":"Rao Xiong","doi":"10.1145/3345094.3345104","DOIUrl":"https://doi.org/10.1145/3345094.3345104","url":null,"abstract":"This paper evaluated whether the learning environment can affect students' performance in reading, mathematics and science. Using the data from PISA, the paper analyzed the relationship between having classic literature, books of poetry, and works of art and students' scores in reading, mathematics and science using Hotelling's T-squared test and three-way between-subjects MANOVA.","PeriodicalId":160662,"journal":{"name":"Proceedings of the 4th International Conference on Information and Education Innovations","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124900633","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}
Food additives are used in meat products for food safety, shelf life and food technology reasons. The types and levels of food additives used in processed meats must comply with the state regulations in order to process safe foods for human consumption. Food additive calculator which is a web-based version, provided by The Thai Food and Drug Administration to help the producers in calculating maximum use level of food additives for all kinds of foods including meats, is found too complicated for small entrepreneurs with inadequate knowledge. Web-based version is also not responsive design and display to users view and is not mobile user oriented. Therefore, this research was aimed to develop a user friendly and convenient mobile application on Android Mobile Operating System for calculating maximum permitted level of food additives used for meat products. This mobile application is designed for calculating four kinds of food additives widely used in ten different favorite meat products. The application gives a correct results of maximum level of each food additives based on the type and weight of meat products. Therefore, this application may be beneficial to reduce the health risk from food additive abuse in meat products.
{"title":"Mobile Application Development of Food Additive Calculation for Meat Products","authors":"N. Prapasuwannakul, K. Bussaban","doi":"10.1145/3345094.3345114","DOIUrl":"https://doi.org/10.1145/3345094.3345114","url":null,"abstract":"Food additives are used in meat products for food safety, shelf life and food technology reasons. The types and levels of food additives used in processed meats must comply with the state regulations in order to process safe foods for human consumption. Food additive calculator which is a web-based version, provided by The Thai Food and Drug Administration to help the producers in calculating maximum use level of food additives for all kinds of foods including meats, is found too complicated for small entrepreneurs with inadequate knowledge. Web-based version is also not responsive design and display to users view and is not mobile user oriented. Therefore, this research was aimed to develop a user friendly and convenient mobile application on Android Mobile Operating System for calculating maximum permitted level of food additives used for meat products. This mobile application is designed for calculating four kinds of food additives widely used in ten different favorite meat products. The application gives a correct results of maximum level of each food additives based on the type and weight of meat products. Therefore, this application may be beneficial to reduce the health risk from food additive abuse in meat products.","PeriodicalId":160662,"journal":{"name":"Proceedings of the 4th International Conference on Information and Education Innovations","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127702961","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}
Infectious diseases are a great plague, especially in low and middle income countries. Beyond the actual treatment, an important role is played by early prevention mechanisms, and education of society at large, about existing risks. This paper tackles these two important challenges, describing the current state of the art in this area, and pointing towards the need for both further, more inclusive research, as well as better education in affected countries on infectious diseases.
{"title":"Research on Prediction of Infectious Diseases, their spread via Social Media and their link to Education","authors":"O. T. Aduragba, A. Cristea","doi":"10.1145/3345094.3345118","DOIUrl":"https://doi.org/10.1145/3345094.3345118","url":null,"abstract":"Infectious diseases are a great plague, especially in low and middle income countries. Beyond the actual treatment, an important role is played by early prevention mechanisms, and education of society at large, about existing risks. This paper tackles these two important challenges, describing the current state of the art in this area, and pointing towards the need for both further, more inclusive research, as well as better education in affected countries on infectious diseases.","PeriodicalId":160662,"journal":{"name":"Proceedings of the 4th International Conference on Information and Education Innovations","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121804387","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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