{"title":"moocRP:一个开源分析平台","authors":"Z. Pardos, Kevin Kao","doi":"10.1145/2724660.2724683","DOIUrl":null,"url":null,"abstract":"In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflows as well as a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing data models, all of which can be accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and interactive visualizations are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium institutions and beyond.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":"99 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2015-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":"{\"title\":\"moocRP: An Open-source Analytics Platform\",\"authors\":\"Z. Pardos, Kevin Kao\",\"doi\":\"10.1145/2724660.2724683\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflows as well as a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing data models, all of which can be accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and interactive visualizations are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium institutions and beyond.\",\"PeriodicalId\":20664,\"journal\":{\"name\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"volume\":\"99 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"35\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second (2015) ACM Conference on Learning @ Scale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2724660.2724683\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2724660.2724683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflows as well as a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing data models, all of which can be accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and interactive visualizations are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium institutions and beyond.