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Content Provider | ACM Digital Library |
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Author | Heffernan, Neil T. Williams, Joseph Jay Selent, Doug Ostrow, Korinn S. Van Inwegen, Eric G. Wang, Yan |
Abstract | Researchers invested in K-12 education struggle not just to enhance pedagogy, curriculum, and student engagement, but also to harness the power of technology in ways that will optimize learning. Online learning platforms offer a powerful environment for educational research at scale. The present work details the creation of an automated system designed to provide researchers with insights regarding data logged from randomized controlled experiments conducted within the ASSISTments TestBed. The Assessment of Learning Infrastructure (ALI) builds upon existing technologies to foster a symbiotic relationship beneficial to students, researchers, the platform and its content, and the learning analytics community. ALI is a sophisticated automated reporting system that provides an overview of sample distributions and basic analyses for researchers to consider when assessing their data. ALI's benefits can also be felt at scale through analyses that crosscut multiple studies to drive iterative platform improvements while promoting personalized learning. |
Starting Page | 279 |
Ending Page | 288 |
Page Count | 10 |
File Format | |
ISBN | 9781450341905 |
DOI | 10.1145/2883851.2883872 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2016-04-25 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | The assistments testbed Automated analysis Tools for learning analytics Randomized controlled experiments at scale Assessment of learning infrastructure Universal data reporting |
Content Type | Text |
Resource Type | Article |
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