ERIC Number: ED592962
Record Type: Non-Journal
Publication Date: 2017-Apr-27
Pages: 31
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-
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Available Date: N/A
Evaluating Classroom Lessons within the Common Core Context: Testing Automated Alignment Using Machine Learning
Levin, Stephanie; Tsang, Fannie; Selby, Trevor; Soike, Derek
AERA Online Paper Repository, Paper presented at the Annual Meeting of the American Educational Research Association (San Antonio, TX, Apr 27-May 1, 2017)
This report stems from the Data Innovation in US Education initiative funded by the Bill & Melinda Gates Foundation. The work was led by the Preva Group, in partnership with IMPAQ and Periscopic. The initiative had three overarching goals: to support the Foundation's efforts to better understand the impact of their education investments; to develop methods which can be used by the Foundation and state and local policy-makers to quickly collect and make sense of high quality education data; and to meet the Foundation's objective of providing educators with tools and supports to deliver literacy instruction aligned with the Common Core State Standards. This paper presents the Automated Alignment project, devised to address the third goal.
Descriptors: Automation, Artificial Intelligence, Common Core State Standards, Alignment (Education), Literacy Education, Rating Scales, Scoring Rubrics, Guidelines, Lesson Plans, Elementary Secondary Education
AERA Online Paper Repository. Available from: American Educational Research Association. 1430 K Street NW Suite 1200, Washington, DC 20005. Tel: 202-238-3200; Fax: 202-238-3250; e-mail: subscriptions@aera.net; Web site: http://www.aera.net
Publication Type: Speeches/Meeting Papers; Reports - Research; Tests/Questionnaires
Education Level: Elementary Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
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Author Affiliations: N/A