Abstract
In this paper, we have presented some results on undergraduate student retention using signal processing techniques for classification of the student data. The experiments revealed that the main factor that influences student retention in the Historically Black Colleges and Universities (HBCU) is the cumulative grade point average (GPA). The linear smoothing of the data helped remove the noise spikes in data thereby improving the retention results. The data is decomposed into Haar coefficients that helped accurate classification. The results showed that the HBCU undergraduate student retention corresponds to an average GPA of 2.8597 and the difference of −0.023307. Using this approach we obtained more accurate retention results on training data.
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Jia, JW., Mareboyana, M. (2015). Undergraduate Student Retention Prediction Using Wavelet Decomposition. In: Yang, GC., Ao, SI., Gelman, L. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9804-4_45
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DOI: https://doi.org/10.1007/978-94-017-9804-4_45
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