Performance Prediction for Higher Education Students Using Deep Learning

Shuping Li, Taotang Liu and M. Irfan Uddin

Complexity, 2021, vol. 2021, 1-10

Abstract: Predicting students’ performance is very important in matters related to higher education as well as with regard to deep learning and its relationship to educational data. Prediction of students’ performance provides support in selecting courses and designing appropriate future study plans for students. In addition to predicting the performance of students, it helps teachers and managers to monitor students in order to provide support to them and to integrate the training programs to obtain the best results. One of the benefits of student’s prediction is that it reduces the official warning signs as well as expelling students because of their inefficiency. Prediction provides support to the students themselves through their choice of courses and study plans appropriate to their abilities. The proposed method used deep neural network in prediction by extracting informative data as a feature with corresponding weights. Multiple updated hidden layers are used to design neural network automatically; number of nodes and hidden layers controlled by feed forwarding and backpropagation data are produced by previous cases. The training mode is used to train the system with labeled data from dataset and the testing mode is used for evaluating the system. Mean absolute error (MAE) and root mean squared error (RMSE) with accuracy used for evolution of the proposed method. The proposed system has proven its worth in terms of efficiency through the achieved results in MAE (0.593) and RMSE (0.785) to get the best prediction.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9958203

DOI: 10.1155/2021/9958203

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