SPOC learner's final grade prediction based on a novel sampling batch normalization embedded deep neural network method

doi:10.1007/s11042-022-13628-y

SPOC learner's final grade prediction based on a novel sampling batch normalization embedded deep neural network method

2022-08-11

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DOI：10.1007/s11042-022-13628-y

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Abstract

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Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learners' performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrate the superiority of the proposed method.

Keywords:

Grade prediction

Class balance

SPOC

Deep neural network

Batch normalization

SPOC learner's final grade prediction based on a novel sampling batch normalization embedded deep neural network method

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