Heuristic Technique to Find Optimal Learning Rate of LSTM for Predicting Student Dropout Rate

Author:

Singh Anuradha KumariORCID,Karthikeyan S.

Publisher

Springer Nature Switzerland

Reference14 articles.

1. Tanucci, G., Fasanella, A.: Orientamento e carriera universitaria. Ingressi ed abbandoni in 5 Facoltà dell’Università di Roma “La Sapienza” nel nuovo assetto didattico (2006)

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3. Pascarella, E.T., Terenzini, P.T.: Predicting freshman persistence and voluntary dropout decisions from a theoretical model. J. High. Educ. 51(1), 60–75 (1980)

4. Raju, D., Schumacker, R.: Exploring student characteristics of retention that lead to graduation in higher education using data mining models. J. Coll. Student Retention: Res. Theory Pract. 16(4), 563–591 (2015)

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