{"title":"人工智能驱动的高等教育教育资源推荐系统有效性研究","authors":"Johannes Schrumpf","doi":"10.33965/celda2022_202207c052","DOIUrl":null,"url":null,"abstract":"Digital resources offer a vast assortment of educational opportunities for students in higher education. From 2018 to 2022, a digital study assistant (DSA), named SIDDATA, was developed at three German universities and consequently field-tested. One of the DSA’s features is an AI-driven natural language interface for educational resource recommendation. This paper performs an analysis of the effectiveness of recommendations, by analyzing data generated over the course of two years of DSA usage. We find that although initial user interest is high, only a small percentage of users engage with the recommendation feature. Furthermore, we find that quality of recommendations was perceived as mixed to negative.","PeriodicalId":200458,"journal":{"name":"Proceeedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ON THE EFFECTIVENESS OF AN AI-DRIVEN EDUCATIONAL RESOURCE RECOMMENDATION SYSTEM FOR HIGHER EDUCATION\",\"authors\":\"Johannes Schrumpf\",\"doi\":\"10.33965/celda2022_202207c052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digital resources offer a vast assortment of educational opportunities for students in higher education. From 2018 to 2022, a digital study assistant (DSA), named SIDDATA, was developed at three German universities and consequently field-tested. One of the DSA’s features is an AI-driven natural language interface for educational resource recommendation. This paper performs an analysis of the effectiveness of recommendations, by analyzing data generated over the course of two years of DSA usage. We find that although initial user interest is high, only a small percentage of users engage with the recommendation feature. Furthermore, we find that quality of recommendations was perceived as mixed to negative.\",\"PeriodicalId\":200458,\"journal\":{\"name\":\"Proceeedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2022)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceeedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2022)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33965/celda2022_202207c052\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33965/celda2022_202207c052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ON THE EFFECTIVENESS OF AN AI-DRIVEN EDUCATIONAL RESOURCE RECOMMENDATION SYSTEM FOR HIGHER EDUCATION
Digital resources offer a vast assortment of educational opportunities for students in higher education. From 2018 to 2022, a digital study assistant (DSA), named SIDDATA, was developed at three German universities and consequently field-tested. One of the DSA’s features is an AI-driven natural language interface for educational resource recommendation. This paper performs an analysis of the effectiveness of recommendations, by analyzing data generated over the course of two years of DSA usage. We find that although initial user interest is high, only a small percentage of users engage with the recommendation feature. Furthermore, we find that quality of recommendations was perceived as mixed to negative.
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