Predicting Student Success in Higher Education with Social, Economic, and Education Variables Using Machine Learning Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3302-6_17
Reference13 articles.
1. Alyahyan E, Düştegör D (2020) Predicting academic success in higher education: literature review and best practices. Int J Educ Technol High Educ 17:3. https://doi.org/10.1186/s41239-020-0177-7
2. Bojorque R, Pesántez-Avilés F (2020) Academic quality management system audit using artificial intelligence techniques. In: Ahram T (eds) Advances in artificial intelligence, software and systems engineering. AHFE 2019. Advances in intelligent systems and computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_28
3. Larrabee Sønderlund A, Hughes E, Smith J (2019) The efficacy of learning analytics interventions in higher education: a systematic review. Br J Educ Technol 50:2594–2618. https://doi.org/10.1111/bjet.12720
4. Aturupane H, Glewwe P, Wisniewski S (2013) The impact of school quality, socioeconomic factors, and child health on students’ academic performance: evidence from Sri Lankan primary schools. Educ Econ 21:1, 2–37. https://doi.org/10.1080/09645292.2010.511852
5. Lim P, Gemici S, Karmel T (2014) The impact of school academic quality on low socioeconomic status students. Aust Econ Rev 47:100–106. https://doi.org/10.1111/1467-8462.12055
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