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}
There are many different examples of where MOOCs have been integrated into teaching and learning in a higher education context. These approaches are typically called blended MOOCs (bMOOCs) and are not intended to replace traditional learning methods but rather to enhance them. Despite increasing interest in bMOOCs there have been few attempts to date to describe with breadth the different ways in which they have been integrated with formal teaching and learning, this means that there are few guides for practitioners, and that it is difficult for the research community to compare different examples. This paper proposes a hierarchy classification of how blended MOOCs are used by presenting a systematic literature review leading to an analysis of 20 different case studies, which is then validated by an independent expert review. The resulting classification model differentiates between Supplementary and Integrated bMOOCs, where Integrated can itself be broken down into models that focus on Content, Assessment, or Interaction. Our work shows that there are at least eight different models for using bMOOCs within formal teaching and learning, although most of the existing research focuses on the Flipped Classroom model (a sub-type of the Content model). Our work therefore reveals gaps in the current understanding of bMOOCs, and will also help to contextualize and scope future research and analysis.
{"title":"A Classification of How MOOCs Are Used for Blended Learning","authors":"Taghreed Alghamdi, W. Hall, D. Millard","doi":"10.1145/3345094.3345107","DOIUrl":"https://doi.org/10.1145/3345094.3345107","url":null,"abstract":"There are many different examples of where MOOCs have been integrated into teaching and learning in a higher education context. These approaches are typically called blended MOOCs (bMOOCs) and are not intended to replace traditional learning methods but rather to enhance them. Despite increasing interest in bMOOCs there have been few attempts to date to describe with breadth the different ways in which they have been integrated with formal teaching and learning, this means that there are few guides for practitioners, and that it is difficult for the research community to compare different examples. This paper proposes a hierarchy classification of how blended MOOCs are used by presenting a systematic literature review leading to an analysis of 20 different case studies, which is then validated by an independent expert review. The resulting classification model differentiates between Supplementary and Integrated bMOOCs, where Integrated can itself be broken down into models that focus on Content, Assessment, or Interaction. Our work shows that there are at least eight different models for using bMOOCs within formal teaching and learning, although most of the existing research focuses on the Flipped Classroom model (a sub-type of the Content model). Our work therefore reveals gaps in the current understanding of bMOOCs, and will also help to contextualize and scope future research and analysis.","PeriodicalId":160662,"journal":{"name":"Proceedings of the 4th International Conference on Information and Education Innovations","volume":"1 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":"130680109","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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