{"title":"基于推荐的学习管理系统的自适应框架","authors":"Munyaradzi Maravanyika, N. Dlodlo","doi":"10.1109/OI.2018.8535816","DOIUrl":null,"url":null,"abstract":"There are a number of existing frameworks for recommendation systems that have been identified in domains such as e-commerce and tourism. The aspects of user profiles, adaptation and group models from the e-commerce and tourism frameworks can be applied to education provided they are customised through incorporating principles of pedagogy such as behavioural theories. Recommendation systems can be adopted to support personalised / differentiated teaching and learning. The aim of this research is to develop an adaptive recommender-systems-based framework for differentiated teaching and learning on eLearning platforms, that is, learning management systems (LMS). Through a literature review, 40 attributes of personalized learning were identified. The Multi-Attribute Utility Theory (MUAT) was used to identify the 10 top attributes to go in as personalized learning framework components. From a population of 1203 students from College X, a sample of 200 students was purposively selected for the research on the basis of their familiarity with College X's eLearning system. 103 students responded to the questionnaire, representing a response rate of 52%. From the responses of the students, the following top ten (10) attributes were identified for inclusion in the personalised learning platforms: culture, emotional/mental state, socialisation, motivation, learning preferences, prior knowledge, educational background, learning/cognitive style, and navigation and learning goals. A theory-driven adaptive recommender-based framework was derived from a combination of literature review and the attributes derived from the research.","PeriodicalId":331140,"journal":{"name":"2018 Open Innovations Conference (OI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Adaptive Framework for Recommender-Based Learning Management Systems\",\"authors\":\"Munyaradzi Maravanyika, N. Dlodlo\",\"doi\":\"10.1109/OI.2018.8535816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are a number of existing frameworks for recommendation systems that have been identified in domains such as e-commerce and tourism. The aspects of user profiles, adaptation and group models from the e-commerce and tourism frameworks can be applied to education provided they are customised through incorporating principles of pedagogy such as behavioural theories. Recommendation systems can be adopted to support personalised / differentiated teaching and learning. The aim of this research is to develop an adaptive recommender-systems-based framework for differentiated teaching and learning on eLearning platforms, that is, learning management systems (LMS). Through a literature review, 40 attributes of personalized learning were identified. The Multi-Attribute Utility Theory (MUAT) was used to identify the 10 top attributes to go in as personalized learning framework components. From a population of 1203 students from College X, a sample of 200 students was purposively selected for the research on the basis of their familiarity with College X's eLearning system. 103 students responded to the questionnaire, representing a response rate of 52%. From the responses of the students, the following top ten (10) attributes were identified for inclusion in the personalised learning platforms: culture, emotional/mental state, socialisation, motivation, learning preferences, prior knowledge, educational background, learning/cognitive style, and navigation and learning goals. A theory-driven adaptive recommender-based framework was derived from a combination of literature review and the attributes derived from the research.\",\"PeriodicalId\":331140,\"journal\":{\"name\":\"2018 Open Innovations Conference (OI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Open Innovations Conference (OI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OI.2018.8535816\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Open Innovations Conference (OI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OI.2018.8535816","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Adaptive Framework for Recommender-Based Learning Management Systems
There are a number of existing frameworks for recommendation systems that have been identified in domains such as e-commerce and tourism. The aspects of user profiles, adaptation and group models from the e-commerce and tourism frameworks can be applied to education provided they are customised through incorporating principles of pedagogy such as behavioural theories. Recommendation systems can be adopted to support personalised / differentiated teaching and learning. The aim of this research is to develop an adaptive recommender-systems-based framework for differentiated teaching and learning on eLearning platforms, that is, learning management systems (LMS). Through a literature review, 40 attributes of personalized learning were identified. The Multi-Attribute Utility Theory (MUAT) was used to identify the 10 top attributes to go in as personalized learning framework components. From a population of 1203 students from College X, a sample of 200 students was purposively selected for the research on the basis of their familiarity with College X's eLearning system. 103 students responded to the questionnaire, representing a response rate of 52%. From the responses of the students, the following top ten (10) attributes were identified for inclusion in the personalised learning platforms: culture, emotional/mental state, socialisation, motivation, learning preferences, prior knowledge, educational background, learning/cognitive style, and navigation and learning goals. A theory-driven adaptive recommender-based framework was derived from a combination of literature review and the attributes derived from the research.