Mining, return migration and gender in the Peruvian Andes
belonging in a transforming comunidad campesina
Shared Spaces
Juliette Bonneviot, Niklas Büscher, Marieta Chirulescu, Dis, Anne Gräfe, Michael Hakimi, Joe Hamilton, Jan Hoeft, Internet TBD, Daniel Keller, Daniel Kiss, Florian Meisenberg, Holger Otten, Aude Pariset, Esther Poppe, Merle Radtke, Nisaar Ulama, Ellen Wagner, Julia Weißenberg, Malte Zander, Benjamin Zuber
Standort:
Hofbibliothek Aschaffenburg
Fernleihe:
uneingeschränkte Fernleihe, Kopie und Ausleihe
Standort:
Bayerische Staatsbibliothek
Fernleihe:
uneingeschränkte Fernleihe, Kopie und Ausleihe
Standort:
Universitätsbibliothek Würzburg
Fernleihe:
uneingeschränkte Fernleihe, Kopie und Ausleihe
Ambivalenzen des geistlichen Spiels
Revisionen von Texten und Methoden
Mining, return migration and gender in the peruvian andes
belonging in a transforming comunidad campesina
Ambivalenzen des geistlichen Spiels
Revisionen von Texten und Methoden
Early detection of students at risk
predicting student dropouts using administrative student data and machine learning methods
High rates of student attrition in tertiary education are a major concern for universities and public policy, as dropout is not only costly for the students but also wastes public funds. To successfully reduce student attrition, it is imperative to...
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Standort:
ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
Signatur:
DS 390 (2018,6)
Fernleihe:
keine Fernleihe
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High rates of student attrition in tertiary education are a major concern for universities and public policy, as dropout is not only costly for the students but also wastes public funds. To successfully reduce student attrition, it is imperative to understand which students are at risk of dropping out and what are the underlying determinants of dropout. We develop an early detection system (EDS) that uses machine learning and classic regression techniques to predict student success in tertiary education as a basis for a targeted intervention. The method developed in this paper is highly standardized and can be easily implemented in every German institution of higher education, as it uses student performance and demographic data collected, stored, and maintained by legal mandate at all German universities and therefore self-adjusts to the university where it is employed. The EDS uses regression analysis and machine learning methods, such as neural networks, decision trees and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. The EDS we present is tested and applied on a medium-sized state university with 23,000 students and a medium-sized private university of applied sciences with 6,700 students. Both institutes of higher education differ considerably in their organization, tuition fees and student-teacher ratios. Our results indicate a prediction accuracy at the end of the first semester of 79% for the state university and 85% for the private university of applied sciences. Furthermore, accuracy of the EDS increases with each completed semester as new performance data becomes available. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences. At the day of enrollment the accuracy, relying only on demographic data, is 68% for the state university and 67% for the private university.
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