Score normalization for a university grades input system using a neural network
A university grades input system requires for professors to enter the normalized total scores for the letter grades and to input the scores from six fields such as Midterm, Final, Quiz which sum up to the total. All six fields have specified bounds which add up to 100. Professors should scale in the total scores to match up the letter grades and scale in every field of each student's original scores within the bounds to sum up to the scaled total score. We solve this problem by a novel design of simple shallow neural network.
M. Hagan, H. Demuth, M. Beale and O. Jesu ́s, Neural network design, 2nd edition, ebook, https://hagan.okstate.edu/NNDesign.pdf (Google Scholar)
S. Marsland, Machine learning, an algorithmic perspective, second edition, CRC press, 2015 (Google Scholar)
E. Matthes, Python crash course, no starch press, 2016 (Google Scholar)
A. Ng, Machine learning lectures, Youtube channel Artificial Intelligence - All in One, 2016 (Google Scholar)
Y.H. Park, Jupyter notebook with a sample data for this article, https://deepmath.kangwon.ac.kr/∼yhpark/pub/grading.zip, 2020 (Google Scholar)
Y.H. Park, Derivatives in neural networks, unpublished note, https://deepmath.kangwon.ac.kr/∼yhpark/pub/derivativesinNN.pdf, 2019 (Google Scholar)
- There are currently no refbacks.
ISSN: 1976-8605 (Print), 2288-1433 (Online)
Copyright(c) 2013 By The Kangwon-Kyungki Mathematical Society, Department of Mathematics, Kangwon National University Chuncheon 21341, Korea Fax: +82-33-259-5662 E-mail: firstname.lastname@example.org