L. Daniela, Anna Visvizi, Miltiadis Demetrios Lytras
{"title":"第一章如何预测不可预测:高等教育中的技术强化学习与学习创新","authors":"L. Daniela, Anna Visvizi, Miltiadis Demetrios Lytras","doi":"10.1108/978-1-78756-555-520181003","DOIUrl":null,"url":null,"abstract":"Abstract \nThe digitisation of various parts of society is developing at an increasingly rapid rate, which effects the way people receive services and how familiar they are with new innovations and technological challenges in educational environments. The future members of society must be prepared to participate in the creation of new innovative solutions as well as to support the process of knowledge creation. Despite the fact that rapid development of technologies requires society to immediately respond, even under these circumstances society must be able to make meaningful decisions. These processes put on the forefront of competence have the ability to predict the unpredictable, which means that the educational environment must to a certain extent be able to predict what has not yet existed and cannot be verified. However, it must at the same time involve specialists from different fields who must deal with technologies and technological solutions that have not been tested or proven consistent in the long run, measure their impact and predict what services are up to date and what technologies and skills are required. This section examines the conditions for technology-enhanced learning (TEL) in a higher education (HE) context to understand what technologies and digital solutions have traditionally been used as well as the role of educators in driving TEL. The aim of the research is to conduct a pilot study to identify the most recent trends in the use of technologies in HE, identify the future research directions, predict the future directions of development and collect and analyse the obtained data.","PeriodicalId":409689,"journal":{"name":"The Future of Innovation and Technology in Education: Policies and Practices for Teaching and Learning Excellence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Chapter 1 How to Predict the Unpredictable: Technology-enhanced Learning and Learning Innovations in Higher Education\",\"authors\":\"L. 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Chapter 1 How to Predict the Unpredictable: Technology-enhanced Learning and Learning Innovations in Higher Education
Abstract
The digitisation of various parts of society is developing at an increasingly rapid rate, which effects the way people receive services and how familiar they are with new innovations and technological challenges in educational environments. The future members of society must be prepared to participate in the creation of new innovative solutions as well as to support the process of knowledge creation. Despite the fact that rapid development of technologies requires society to immediately respond, even under these circumstances society must be able to make meaningful decisions. These processes put on the forefront of competence have the ability to predict the unpredictable, which means that the educational environment must to a certain extent be able to predict what has not yet existed and cannot be verified. However, it must at the same time involve specialists from different fields who must deal with technologies and technological solutions that have not been tested or proven consistent in the long run, measure their impact and predict what services are up to date and what technologies and skills are required. This section examines the conditions for technology-enhanced learning (TEL) in a higher education (HE) context to understand what technologies and digital solutions have traditionally been used as well as the role of educators in driving TEL. The aim of the research is to conduct a pilot study to identify the most recent trends in the use of technologies in HE, identify the future research directions, predict the future directions of development and collect and analyse the obtained data.