6. רשימת מקורות

עמוד:35

35 Sara, N . B . , Halland, R . , Igel, C . , & Alstrup, S . ( 2015 ) . High - school dropout prediction using machine learning : A danish large - scale study . In M . Verleysen ( Ed . ) , ESANN 2015 Proceedings : 23 rd European symposium on artificial neural networks, computational intelligence and machine learning : Bruges – April 22 - 23 - 24, 2015 ( pp . 319 - 24 ) . Schoeneberger, J . A . ( 2011 ) . Longitudinal attendance patterns : Developing high school dropouts . The Clearing House : A Journal of Educational Strategies, Issues and Ideas, . 14 - 7 , ) 1 ( 85 UNESCO Institute for Statistics & Centre for Policy Research . ( 2016, July ) . A Pilot Study of Estimating Out - Of - School Children in India . Montréal, Canada . UNESCO Institute for Statistics ( 2019, October ) . Combining Data on Out - of - school Children, Completion and Learning to Offer a More Comprehensive View on SDG 4 Information Paper No . 61 ) . Montréal, Canada ( UNESCO Institute for Statistics & UNICEF ( 2015 ) . Fixing the Broken Promise of Education for All : Findings from the Global Initiative on Out - of - School Children . Montréal, Canada : Author . UNESCO . ( 2015 ) . Education for All 2000 - 2015 : Achievements and challenges . Paris, France . UNICEF & UNESCO Institute for Statistics . ( 2016 ) . UNICEF series on education participation and dropout prevention : Vol I . Monitoring education participation : Framework for monitoring children and adolescents who are out of school or at risk of dropping out . Geneva, switzerland .

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