Christian Fischer, Z. Pardos, R. Baker, J. Williams, P. Smyth, Renzhe Yu, Stefan Slater, Rachel B. Baker, M. Warschauer
{"title":"Mining Big Data in Education: Affordances and Challenges","authors":"Christian Fischer, Z. Pardos, R. Baker, J. Williams, P. Smyth, Renzhe Yu, Stefan Slater, Rachel B. Baker, M. Warschauer","doi":"10.3102/0091732X20903304","DOIUrl":null,"url":null,"abstract":"The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.","PeriodicalId":47753,"journal":{"name":"Review of Research in Education","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3102/0091732X20903304","citationCount":"159","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Review of Research in Education","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.3102/0091732X20903304","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 159
Abstract
The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.
期刊介绍:
Review of Research in Education (RRE), published annually since 1973 (approximately 416 pp./volume year), provides an overview and descriptive analysis of selected topics of relevant research literature through critical and synthesizing essays. Articles are usually solicited for specific RRE issues. There may also be calls for papers. RRE promotes discussion and controversy about research problems in addition to pulling together and summarizing the work in a field.