1. Acikkar, M., & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36(3 PART 2), 7228–7233. https://doi.org/10.1016/j.eswa.2008.09.007.
2. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Press.
3. Alexander, R., Rose, J., & Woodhead, C. (1992). Curriculum organisation and classroom practice in primary schools: A discussion paper. The Department of Education and Science of United Kingdom.
4. Alkhasawneh, R., & Hargraves, R. H. (2014). Developing a hybrid model to predict student first year retention in STEM disciplines using machine learning techniques. Journal of STEM Education: Innovations & Research, 15(3), 35–42. https://core.ac.uk/download/pdf/51289621.pdf.
5. Aluko, R. O., Adenuga, O. A., Kukoyi, P. O., Soyingbe, A. A., & Oyedeji, J. O. (2016). Predicting the academic success of architecture students by pre-enrolment requirement: Using machine-learning techniques. Construction Economics and Building, 16(4), 86–98. https://doi.org/10.5130/AJCEB.v16i4.5184