Expanding Evidence Approaches for Learning in a Digital World / Barbara Means and Kea Anderson.
This report describes how big data and an evidence framework can align across five contexts of educational improvement. It explains that before working with big data, there is an important prerequisite: the proposed innovation should align with deeper learning objectives and should incorporate sound...
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| Format: | eBook |
| Language: | English |
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Distributed by ERIC Clearinghouse,
2013.
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MARC
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| 245 | 1 | 0 | |a Expanding Evidence Approaches for Learning in a Digital World / |c Barbara Means and Kea Anderson. |
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| 520 | |a This report describes how big data and an evidence framework can align across five contexts of educational improvement. It explains that before working with big data, there is an important prerequisite: the proposed innovation should align with deeper learning objectives and should incorporate sound learning sciences principles. New curriculum standards, such as the Common Core State Standards and the Next Generation Science Standards, emphasize deeper learning objectives. Unless these are substantively addressed at the core of a learning resource, it is unlikely the resource will meet these important objectives. Likewise, a proposed innovation is more likely to succeed if it is grounded in fundamental principles of how people learn. Once these prerequisites are met, the evidence framework describes five opportunities for utilizing big data, each in a different educational context: (1) During development of an innovative learning resource, educational data mining and learning analytics can uncover patterns of learner behavior that can be used to guide improvement. (2) As learners use a digital resource, adaptive learning systems can personalize learning by using big data with new evidence models. (3) As institutions try to support struggling students, big data and new data analysis techniques can help guide intervention. (4) As educational systems assess student achievement, big data and new evidence models can shift measurements to focus more on what is really important and to provide more timely information to educators and students. (5) As educators choose and adapt learning resources from the vast array now offered on the Internet, big data and new evidence models can inform their choices. The ideas presented in this report have implications for learning technology developers, consumers, education researchers, policymakers, and research funders. The Technical Working Group of researchers and policymakers who provided input and guidance for this evidence framework also developed a set of recommendations for putting the framework into action. The resulting 14 recommendations for capitalizing on new approaches to evidence as digital resources are provided. The report also includes cautionary notes about the ethical issues that must be tackled in handling student data. | ||
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| 650 | 0 | 7 | |a Technology Uses in Education. |2 ericd. |
| 650 | 0 | 7 | |a Educational Resources. |2 ericd. |
| 650 | 0 | 7 | |a Individualized Instruction. |2 ericd. |
| 650 | 0 | 7 | |a Student Needs. |2 ericd. |
| 650 | 0 | 7 | |a Computer Assisted Testing. |2 ericd. |
| 650 | 0 | 7 | |a Evaluation Methods. |2 ericd. |
| 650 | 0 | 7 | |a Decision Making. |2 ericd. |
| 650 | 0 | 7 | |a Data Collection. |2 ericd. |
| 650 | 0 | 7 | |a Data Analysis. |2 ericd. |
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| 650 | 0 | 7 | |a Research and Development. |2 ericd. |
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