@phdthesis{sarmonpal_learning_2018,
	address = {United States -- California},
	type = {Ed.{D}.},
	title = {Learning {Analytics} from {Research} to {Practice}: {A} {Content} {Analysis} to {Assess} {Information} {Quality} on {Product} {Websites}},
	copyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},
	shorttitle = {Learning {Analytics} from {Research} to {Practice}},
	url = {https://search.proquest.com/eric/docview/2157976960/abstract/D691669D3D71409CPQ/11},
	abstract = {The purpose of this study was to examine and describe the nature of the research to practice gap in learning analytics applications in K12 educational settings. It was also the purpose of this study to characterize how learning analytics are currently implemented and understood. A secondary objective of this research was to advance a preliminary learning analytics implementation framework for practitioners. To achieve these purposes, this study applied quantitative content analysis using automated text analysis techniques to assess the quality of information provided on analytics-based product websites against learning analytics research. Because learning analytics implementations require adoption of analytical tools, characterizing content on analytics-based product websites provides insight into data practices in K12 schools and how learning analytics are practiced and understood. A major finding of this study was that learning analytics do not appear to be applied in ways that will improve learning outcomes for students as described by the research. A second finding was that policy influence expressed in the study corpus suggest competing interests within the current policy structure for K12 education settings. Keywords: quantitative content analysis, automated text analysis, learning analytics, big data, frameworks, educational technology, website content analysis},
	language = {EN},
	urldate = {2019-01-10},
	school = {Pepperdine University},
	author = {Sarmonpal, Sandra},
	year = {2018},
	keywords = {Applied sciences, Automated text analysis, Big data, Content analysis, Education, Educational technology, Learning analytics, Websites},
}

{"_id":"bhk88A7SeWbKE2gmK","bibbaseid":"sarmonpal-learninganalyticsfromresearchtopracticeacontentanalysistoassessinformationqualityonproductwebsites-2018","author_short":["Sarmonpal, S."],"bibdata":{"bibtype":"phdthesis","type":"Ed.D.","address":"United States – California","title":"Learning Analytics from Research to Practice: A Content Analysis to Assess Information Quality on Product Websites","copyright":"Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.","shorttitle":"Learning Analytics from Research to Practice","url":"https://search.proquest.com/eric/docview/2157976960/abstract/D691669D3D71409CPQ/11","abstract":"The purpose of this study was to examine and describe the nature of the research to practice gap in learning analytics applications in K12 educational settings. It was also the purpose of this study to characterize how learning analytics are currently implemented and understood. A secondary objective of this research was to advance a preliminary learning analytics implementation framework for practitioners. To achieve these purposes, this study applied quantitative content analysis using automated text analysis techniques to assess the quality of information provided on analytics-based product websites against learning analytics research. Because learning analytics implementations require adoption of analytical tools, characterizing content on analytics-based product websites provides insight into data practices in K12 schools and how learning analytics are practiced and understood. A major finding of this study was that learning analytics do not appear to be applied in ways that will improve learning outcomes for students as described by the research. A second finding was that policy influence expressed in the study corpus suggest competing interests within the current policy structure for K12 education settings. Keywords: quantitative content analysis, automated text analysis, learning analytics, big data, frameworks, educational technology, website content analysis","language":"EN","urldate":"2019-01-10","school":"Pepperdine University","author":[{"propositions":[],"lastnames":["Sarmonpal"],"firstnames":["Sandra"],"suffixes":[]}],"year":"2018","keywords":"Applied sciences, Automated text analysis, Big data, Content analysis, Education, Educational technology, Learning analytics, Websites","bibtex":"@phdthesis{sarmonpal_learning_2018,\n\taddress = {United States -- California},\n\ttype = {Ed.{D}.},\n\ttitle = {Learning {Analytics} from {Research} to {Practice}: {A} {Content} {Analysis} to {Assess} {Information} {Quality} on {Product} {Websites}},\n\tcopyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},\n\tshorttitle = {Learning {Analytics} from {Research} to {Practice}},\n\turl = {https://search.proquest.com/eric/docview/2157976960/abstract/D691669D3D71409CPQ/11},\n\tabstract = {The purpose of this study was to examine and describe the nature of the research to practice gap in learning analytics applications in K12 educational settings. It was also the purpose of this study to characterize how learning analytics are currently implemented and understood. A secondary objective of this research was to advance a preliminary learning analytics implementation framework for practitioners. To achieve these purposes, this study applied quantitative content analysis using automated text analysis techniques to assess the quality of information provided on analytics-based product websites against learning analytics research. Because learning analytics implementations require adoption of analytical tools, characterizing content on analytics-based product websites provides insight into data practices in K12 schools and how learning analytics are practiced and understood. A major finding of this study was that learning analytics do not appear to be applied in ways that will improve learning outcomes for students as described by the research. A second finding was that policy influence expressed in the study corpus suggest competing interests within the current policy structure for K12 education settings. 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