Andrew Koster, T. Primo, F. Koch, A. Oliveira, Hyunkwon Chung
{"title":"Towards an Educator-Centred Digital Teaching Platform: The Ground Conditions for a Data-Driven Approach","authors":"Andrew Koster, T. Primo, F. Koch, A. Oliveira, Hyunkwon Chung","doi":"10.1109/ICALT.2015.124","DOIUrl":null,"url":null,"abstract":"We introduce innovations in a Digital Teaching Platform (DTP) through tools centred on supporting the teacher. We focus on the utilisation of data about the students and the class in order to recommend actions and content for the teacher. For this, we need a platform with novel capabilities. First, we augment the content delivery application with data collecting capabilities. Second, we create a cloud-based analytics engine that infers student profiles and context parameters from multi-modal sources. Third, we provide a web-based platform for content composition that makes use of the inferred student and context profiles to support teachers in lesson planning. Our solution implements the complete cycle from content composition to delivery and adjustment, allowing for the research and development of new features and intelligences in Digital Education.","PeriodicalId":170914,"journal":{"name":"2015 IEEE 15th International Conference on Advanced Learning Technologies","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 15th International Conference on Advanced Learning Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2015.124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
We introduce innovations in a Digital Teaching Platform (DTP) through tools centred on supporting the teacher. We focus on the utilisation of data about the students and the class in order to recommend actions and content for the teacher. For this, we need a platform with novel capabilities. First, we augment the content delivery application with data collecting capabilities. Second, we create a cloud-based analytics engine that infers student profiles and context parameters from multi-modal sources. Third, we provide a web-based platform for content composition that makes use of the inferred student and context profiles to support teachers in lesson planning. Our solution implements the complete cycle from content composition to delivery and adjustment, allowing for the research and development of new features and intelligences in Digital Education.