{"title":"基于图的可视化主题依赖模型:支持大规模的评估设计和交付","authors":"K. Cooper, Hassan Khosravi","doi":"10.1145/3170358.3170418","DOIUrl":null,"url":null,"abstract":"Educational environments continue to rapidly evolve to address the needs of diverse, growing student populations, while embracing advances in pedagogy and technology. In this changing landscape ensuring the consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about students' achievements, and tracking students' progression over time are all challenging tasks, particularly at scale. Here, a collection of visual Topic Dependency Models (TDMs) is proposed to help address these challenges. It visualises the required topics and their dependencies at a course level (e.g., CS 100) and assessment achievement data at the classroom level (e.g., students in CS 100 Term 1 2016 Section 001) both at one point in time (static) and over time (dynamic). The collection of TDMs share a common, two-weighted graph foundation. An algorithm is presented to create a TDM (static achievement for a cohort). An open-source, proof of concept implementation of the TDMs is under development; the current version is described briefly in terms of its support for visualising existing (historical, test) and synthetic data generated on demand.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Graph-based visual topic dependency models: supporting assessment design and delivery at scale\",\"authors\":\"K. Cooper, Hassan Khosravi\",\"doi\":\"10.1145/3170358.3170418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Educational environments continue to rapidly evolve to address the needs of diverse, growing student populations, while embracing advances in pedagogy and technology. In this changing landscape ensuring the consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about students' achievements, and tracking students' progression over time are all challenging tasks, particularly at scale. Here, a collection of visual Topic Dependency Models (TDMs) is proposed to help address these challenges. It visualises the required topics and their dependencies at a course level (e.g., CS 100) and assessment achievement data at the classroom level (e.g., students in CS 100 Term 1 2016 Section 001) both at one point in time (static) and over time (dynamic). The collection of TDMs share a common, two-weighted graph foundation. An algorithm is presented to create a TDM (static achievement for a cohort). An open-source, proof of concept implementation of the TDMs is under development; the current version is described briefly in terms of its support for visualising existing (historical, test) and synthetic data generated on demand.\",\"PeriodicalId\":437369,\"journal\":{\"name\":\"Proceedings of the 8th International Conference on Learning Analytics and Knowledge\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-03-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th International Conference on Learning Analytics and Knowledge\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3170358.3170418\",\"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 8th International Conference on Learning Analytics and Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3170358.3170418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph-based visual topic dependency models: supporting assessment design and delivery at scale
Educational environments continue to rapidly evolve to address the needs of diverse, growing student populations, while embracing advances in pedagogy and technology. In this changing landscape ensuring the consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about students' achievements, and tracking students' progression over time are all challenging tasks, particularly at scale. Here, a collection of visual Topic Dependency Models (TDMs) is proposed to help address these challenges. It visualises the required topics and their dependencies at a course level (e.g., CS 100) and assessment achievement data at the classroom level (e.g., students in CS 100 Term 1 2016 Section 001) both at one point in time (static) and over time (dynamic). The collection of TDMs share a common, two-weighted graph foundation. An algorithm is presented to create a TDM (static achievement for a cohort). An open-source, proof of concept implementation of the TDMs is under development; the current version is described briefly in terms of its support for visualising existing (historical, test) and synthetic data generated on demand.