{"title":"作为一名学生,工程教育创新能为你做些什么?作为一名学生,你能为工程教育创新做些什么?","authors":"Carlos Alario-Hoyos","doi":"10.26439/ciis2019.5498","DOIUrl":null,"url":null,"abstract":"Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decision-making processes. Today it is possible to collect data from many more stakeholders(not just students), and also to collect much more data from each stakeholder. Nevertheless, low-level data collected by monitoring the interactions of the multiple stakeholders with learning platforms and other computing systems must be transformed into meaningful high-level indicators and visualizations that guide decision-making processes. The aim of this paper is to discuss some notable trends in data-driven innovation in engineering education, including 1) improvement of educational content; 2) improvement of learners’ social interactions; 3) improvement of learners’ self-regulated learning skills; and 4) prediction of learners’ behavior. However, there are also significant risks associated with data collection and processing, such as privacy, transparency, biases, misinterpretations, etc., which must also be taken into account, and require creating specialized units and training the personnel in data management.","PeriodicalId":365289,"journal":{"name":"Innovando la educación en tecnología. Actas del II Congreso Internacional de Ingeniería de Sistemas","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What Can Innovation in Engineering Education Do for You as a Student and What can You Do as a Student for Innovation in Engineering Education?\",\"authors\":\"Carlos Alario-Hoyos\",\"doi\":\"10.26439/ciis2019.5498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decision-making processes. Today it is possible to collect data from many more stakeholders(not just students), and also to collect much more data from each stakeholder. Nevertheless, low-level data collected by monitoring the interactions of the multiple stakeholders with learning platforms and other computing systems must be transformed into meaningful high-level indicators and visualizations that guide decision-making processes. The aim of this paper is to discuss some notable trends in data-driven innovation in engineering education, including 1) improvement of educational content; 2) improvement of learners’ social interactions; 3) improvement of learners’ self-regulated learning skills; and 4) prediction of learners’ behavior. However, there are also significant risks associated with data collection and processing, such as privacy, transparency, biases, misinterpretations, etc., which must also be taken into account, and require creating specialized units and training the personnel in data management.\",\"PeriodicalId\":365289,\"journal\":{\"name\":\"Innovando la educación en tecnología. Actas del II Congreso Internacional de Ingeniería de Sistemas\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Innovando la educación en tecnología. Actas del II Congreso Internacional de Ingeniería de Sistemas\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26439/ciis2019.5498\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Innovando la educación en tecnología. Actas del II Congreso Internacional de Ingeniería de Sistemas","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26439/ciis2019.5498","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
What Can Innovation in Engineering Education Do for You as a Student and What can You Do as a Student for Innovation in Engineering Education?
Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decision-making processes. Today it is possible to collect data from many more stakeholders(not just students), and also to collect much more data from each stakeholder. Nevertheless, low-level data collected by monitoring the interactions of the multiple stakeholders with learning platforms and other computing systems must be transformed into meaningful high-level indicators and visualizations that guide decision-making processes. The aim of this paper is to discuss some notable trends in data-driven innovation in engineering education, including 1) improvement of educational content; 2) improvement of learners’ social interactions; 3) improvement of learners’ self-regulated learning skills; and 4) prediction of learners’ behavior. However, there are also significant risks associated with data collection and processing, such as privacy, transparency, biases, misinterpretations, etc., which must also be taken into account, and require creating specialized units and training the personnel in data management.