ERIC ED542777: Using Big Data To Predict Student Dropouts: Technology Affordances For Research - Info and Reading Options
By ERIC
"ERIC ED542777: Using Big Data To Predict Student Dropouts: Technology Affordances For Research" and the language of the book is English.
“ERIC ED542777: Using Big Data To Predict Student Dropouts: Technology Affordances For Research” Metadata:
- Title: ➤ ERIC ED542777: Using Big Data To Predict Student Dropouts: Technology Affordances For Research
- Author: ERIC
- Language: English
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- Internet Archive ID: ERIC_ED542777
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"ERIC ED542777: Using Big Data To Predict Student Dropouts: Technology Affordances For Research" Description:
The Internet Archive:
An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student successes and failures. We found that a pattern of declining performance over time is a good predictor of the likelihood of dropping out, and that having dependents or being married or in the military reduces the risk of dropping out. The risk of dropping out was higher for older students, females, and students with previous college education or transfer credits. These results provide a foundation for testing interventions to help students who are at risk and will also help to inform the development of a "research pipeline" that will enable rapid experimental studies of new tools and strategies. (Contains 1 table.) [For the complete proceedings, "Proceedings of the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age (CELDA) (Madrid, Spain, Oct 19-21, 2012)," see ED542606.]
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"ERIC ED542777: Using Big Data To Predict Student Dropouts: Technology Affordances For Research" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 2.99 Mbs, and the file-s went public at Sun Mar 27 2016.
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