Binary logistic regression classifying the gender of student towards computer learning in European schools

Verma, Chaman and Stoffová, Veronika and Illés, Zoltán and Dahiya, Sanjay: Binary logistic regression classifying the gender of student towards computer learning in European schools. In: Conference of PhD Students in Computer Science, (11). pp. 45-48. (2018)

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Abstract

The authors presented a gender prediction model of student based on answers provided into survey during academic year 2011 in Europe. This experimental study is performed in R-language by applying logistic regression on the large data-set available on the website of European Commission. More than 2500 schools, 27 countries, and more than 45000 students have participated in the survey held in 2011 and survey was conducted by European Commission on primary schools whose were studying at ISCED level 3 (upper secondary level of education). The dichotomous variable is gender and 6 predictors belong to attitude towards computer learning. The best cut-off and accuracy of the presented model is measured 0.499 and 0.628 respectively at 0.5 thresholds using Receiver Operating Characteristics (ROC) and Area under the curve (AUC) which signifies the model to predict more females with correctly as compared to males.

Item Type: Article
Journal or Publication Title: Conference of PhD Students in Computer Science
Date: 2018
Volume: 11
Page Range: pp. 45-48
Event Title: Conference of PhD students in computer science (11.) (2018) (Szeged)
Uncontrolled Keywords: Számítástechnika
Additional Information: Bibliogr.: p. 47-48. ; összefoglalás angol nyelven
Date Deposited: 2019. Oct. 28. 11:24
Last Modified: 2019. Oct. 28. 11:24
URI: http://acta.bibl.u-szeged.hu/id/eprint/61761

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