Geoffrey Angus, Richard Diehl Martinez, M. Stevens, A. Paepcke
{"title":"Via: Illuminating Academic Pathways at Scale","authors":"Geoffrey Angus, Richard Diehl Martinez, M. Stevens, A. Paepcke","doi":"10.1145/3330430.3333623","DOIUrl":null,"url":null,"abstract":"The processes through which course selections accumulate into college pathways in US higher education is poorly instrumented for observation at scale. We offer an analytic toolkit, called Via, which transforms commonly available enrollment data into formal graphs that are amenable to interactive visualizations and computational exploration. We explain the procedures required to project enrollment records onto graphs, and then demonstrate the toolkit utilizing eighteen years of enrollment data at a large private research university. Findings complement prior research on academic search and offer powerful new means for making pathway navigation more efficient.","PeriodicalId":20693,"journal":{"name":"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale","volume":"15 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3330430.3333623","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
The processes through which course selections accumulate into college pathways in US higher education is poorly instrumented for observation at scale. We offer an analytic toolkit, called Via, which transforms commonly available enrollment data into formal graphs that are amenable to interactive visualizations and computational exploration. We explain the procedures required to project enrollment records onto graphs, and then demonstrate the toolkit utilizing eighteen years of enrollment data at a large private research university. Findings complement prior research on academic search and offer powerful new means for making pathway navigation more efficient.