{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/49838"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/49838","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Supporting teachers' data use for instructional improvement: The role of learning performance management systems and professional learning context","abstract":"Teachers are increasingly expected to use data in systematic and scientific ways to make class- and school-level decisions (Kowalski, Lasley, & Mahoney, 2008). Despite great optimism and substantial investments in data use initiatives, recent studies (e.g., Carlson, Borman, & Robinson, 2011) found weak relationships between data use supports, teachers’ data use, and improved teaching and learning. This multiple-methods study used questionnaire data from a statewide sample of teachers (n=1422) to examine key school conditions—technological data management tools and professional learning community context—that purportedly support teachers’ data use. Qualitative methods (observations, interviews, and focus groups) were also employed at one school site to explore influences and consequences of data use. Statistical analysis showed that comprehensive learning performance management systems and professional learning communities support teachers’ data use, but school conditions are only part of the picture. Qualitative findings illustrated the ways in which teachers’ beliefs (e.g., about low-income minority students), knowledge (e.g., about effective instruction), and motivation (e.g., understanding of role and responsibility) drive the extent to which they use data. More importantly, their beliefs, knowledge, and motivation also shape how teachers interpret data and make decisions (e.g., whether learning problems require classroom changes, interventions for certain students, or non-instructional responses). This study challenges the common conceptualization of teachers’ data use as a techno-scientific process that can be mechanized with the right organizational conditions. Findings suggest that data use is better understood as interpretive acts, inevitably shaped by individual and groups of people with particular beliefs, knowledge, and motivation. While tools, methods, and procedures have some value for enhancing data use, they do not guarantee improved educational quality and equity. Data use initiatives need to support educators in interpreting data critically and making good professional judgments specific to their school, community, and classroom contexts.","abstract_html":"Teachers are increasingly expected to use data in systematic and scientific ways to make class- and school-level decisions (Kowalski, Lasley, & Mahoney, 2008). Despite great optimism and substantial investments in data use initiatives, recent studies (e.g., Carlson, Borman, & Robinson, 2011) found weak relationships between data use supports, teachers’ data use, and improved teaching and learning. This multiple-methods study used questionnaire data from a statewide sample of teachers (n=1422) to examine key school conditions—technological data management tools and professional learning community context—that purportedly support teachers’ data use. Qualitative methods (observations, interviews, and focus groups) were also employed at one school site to explore influences and consequences of data use. Statistical analysis showed that comprehensive learning performance management systems and professional learning communities support teachers’ data use, but school conditions are only part of the picture. Qualitative findings illustrated the ways in which teachers’ beliefs (e.g., about low-income minority students), knowledge (e.g., about effective instruction), and motivation (e.g., understanding of role and responsibility) drive the extent to which they use data. More importantly, their beliefs, knowledge, and motivation also shape how teachers interpret data and make decisions (e.g., whether learning problems require classroom changes, interventions for certain students, or non-instructional responses). This study challenges the common conceptualization of teachers’ data use as a techno-scientific process that can be mechanized with the right organizational conditions. Findings suggest that data use is better understood as interpretive acts, inevitably shaped by individual and groups of people with particular beliefs, knowledge, and motivation. While tools, methods, and procedures have some value for enhancing data use, they do not guarantee improved educational quality and equity. Data use initiatives need to support educators in interpreting data critically and making good professional judgments specific to their school, community, and classroom contexts.","abstract_has_math":false,"creators":["Gandha, Tysza"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Educational Psychology","degree_department":null,"school":null,"contributors":["Ryan, Katherine E.","Hood, Stafford","Schwandt, Thomas A.","Trent, William T."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-30T17:20:21Z","date_published":"2014-05-30T17:20:21Z","updated_at":"2026-07-22T22:25:40Z","subjects":["Teachers' data use","data-based decision making in schools","instructional decision making","learning performance management system","data management system"],"languages":["en"],"rights":["Copyright 2014 Tysza Gandha"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/49838","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ryan, Katherine E.","Hood, Stafford","Schwandt, Thomas A.","Trent, William T."]},{"key":"dc:creator","label":"Author","values":["Gandha, Tysza"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-05-30T17:20:21Z","2016-09-22T20:59:26Z","2014-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Educational Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Teachers' data use","data-based decision making in schools","instructional decision making","learning performance management system","data management system"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Tysza Gandha"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/49838"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Teachers are increasingly expected to use data in systematic and scientific ways to make class- and school-level decisions (Kowalski, Lasley, & Mahoney, 2008). 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Qualitative findings illustrated the ways in which teachers’ beliefs (e.g., about low-income minority students), knowledge (e.g., about effective instruction), and motivation (e.g., understanding of role and responsibility) drive the extent to which they use data. More importantly, their beliefs, knowledge, and motivation also shape how teachers interpret data and make decisions (e.g., whether learning problems require classroom changes, interventions for certain students, or non-instructional responses). This study challenges the common conceptualization of teachers’ data use as a techno-scientific process that can be mechanized with the right organizational conditions. Findings suggest that data use is better understood as interpretive acts, inevitably shaped by individual and groups of people with particular beliefs, knowledge, and motivation. While tools, methods, and procedures have some value for enhancing data use, they do not guarantee improved educational quality and equity. 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