Abstract
Grading informs learners and instructors of both current learning ability levels and necessary improvement. For norm referenced grading, the instructors conventionally use a statistical method. This paper proposes an algorithm for the norm referenced grading. Moreover, the rise of artificial intelligence nowadays makes us curious how a machine learning technique is efficient in the norm referenced grading. We therefore compare the statistical method and our algorithm with the machine learning method. The experiment relies on the data sets of both normal and skewed distributions. The comparative evaluation reveals that our algorithm and the machine learning method yield similar grading results in several cases. On the other hand, in overall, the algorithm, machine learning, and statistical methods produce the best, moderate, and lowest grading qualities, respectively.
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Banditwattanawong, T., Masdisornchote, M. (2021). Norm-Referenced Achievement Grading: Methods and Comparison. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_14
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DOI: https://doi.org/10.1007/978-3-030-58669-0_14
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