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dc.contributor.authorMattingly, Karen D.
dc.contributor.authorRice, Margaret C.
dc.contributor.authorBerge, Zane L.
dc.date.accessioned2019-11-07T17:17:12Z
dc.date.available2019-11-07T17:17:12Z
dc.date.issued2012
dc.description.abstractThis paper examines learning and academic analytics and its relevance to distance education in undergraduate and graduate programs as it impacts students and teaching faculty, and also academic institutions. The focus is to explore the measurement, collection, analysis, and reporting of data as predictors of student success and drivers of departmental process and program curriculum. Learning and academic analytics in higher education is used to predict student success by examining how and what students learn and how success is supported by academic programs and institutions. The paper examines what is being done to support students, whether or not it is effective, and if not why, and what educators can do. The paper also examines how these data can be used to create new metrics and inform a continuous cycle of improvement. It presents examples of working models from a sample of institutions of higher education: The Graduate School of Medicine at the University of Wollongong, the University of Michigan, Purdue University, and the University of Maryland, Baltimore County. Finally, the paper identifies considerations and recommendations for using analytics and offer suggestions for future research.en_US
dc.description.urihttps://www.kmel-journal.org/ojs/index.php/online-publication/article/view/168en_US
dc.format.extent12 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2lxiy-pqdj
dc.identifier.citationMattingly, Karen D.; Rice, Margaret C.; Berge, Zane L.; Learning analytics as a tool for closing the assessment loop in higher education; Knowledge Management & E-Learning: An International Journal, Vol.4, No.3; https://doi.org/10.34105/j.kmel.2012.04.020;en_US
dc.identifier.urihttps://doi.org/10.34105/j.kmel.2012.04.020
dc.identifier.urihttp://hdl.handle.net/11603/16213
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Education Department Collection
dc.relation.ispartofUMBC Graduate School
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC College of Engineering and Information Technology Dean's Office
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectacademic analyticsen_US
dc.subjectdata miningen_US
dc.subjectdata warehouseen_US
dc.subjectdistance educationen_US
dc.subjecthigher educationen_US
dc.subjectlearning analyticsen_US
dc.subjectonline learningen_US
dc.subjectpredictive modelingen_US
dc.subjectweb analyticsen_US
dc.subjectUMBC Instructional System Designen_US
dc.titleLearning analytics as a tool for closing the assessment loop in higher educationen_US
dc.typeTexten_US


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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Except where otherwise noted, this item's license is described as This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.