Don't Drop Out, Drop In: A Workshop for At-Risk Students
نویسندگان
چکیده
منابع مشابه
Predicting Students Drop Out: A Case Study
The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific ...
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In the emerging field of educational data mining, a strong bias towards data-rich digital learning environments is the current state of affairs [2, table 2]. However, in many educational institutes a lot of regular course data will probably be more readily available. This data may also be used to support and advise students in various ways, for the better of the student as well as the institute...
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IT students dropping out is a key problem in academic institutions worldwide. Previous research on student dropout has advanced many factor or variance models explaining or predicting why university student drop out. Although these studies increased our understanding of the reasons students drop out of computer science courses, university studies, and online learning, we find the factor or vari...
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This paper presents a scoring model that predicts the risk of drop-out for borrowers at a microfinance lender in Bolivia. Drop-out risk was greater for women, manufacturers, newer borrowers, and those with more arrears. Out-of-sample tests suggest that scoring may help microfinance lenders to detect segments of their clientele (and even specific current clients) who are at-risk of drop-out. Ack...
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ژورنال
عنوان ژورنال: NACADA Journal
سال: 1999
ISSN: 0271-9517,2330-3840
DOI: 10.12930/0271-9517-19.1.50