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Content Provider | ACM Digital Library |
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Author | DeMonbrun, R. Matthew Teasley, Stephanie D. Brown, Michael Geoffrey |
Abstract | Recent research using learning analytics data to explore student performance over the course of a term suggests that a substantial percentage of students who are classified as academically struggling manage to recover. In this study, we report the result of a hazard analysis based on students' behavioral engagement with different digital instructional technologies over the course of a semester. We observe substantially different adoption and use behavior between students who did and did not experience academic difficulty in the course. Students who experienced moderate academic difficulty benefited the most from using tools that helped them plan their study behaviors. Students who experienced more severe academic difficulty benefited from tools that helped them prepare for exams. We observed that students adopted most tools and system features before they experienced academic difficulty, and students who adopted early were more likely to recover. |
Starting Page | 489 |
Ending Page | 493 |
Page Count | 5 |
File Format | |
ISBN | 9781450348706 |
DOI | 10.1145/3027385.3027393 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2017-03-13 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Technology adoption Undergraduate education Educational technology Early warning systems |
Content Type | Text |
Resource Type | Article |
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