Predicting academic performance of students from VLE big data using deep learning models

Abstract

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Predicting Students’ Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine

“…The last part of the analysis is performance comparison between ICGAN-DSVM and related works [14]- [18] which results have been summarized in Fig. 6.…”

“…Support vector machine achieved the highest average accuracy among three, which is about 70%. Differed from shallow learning in [14]- [17], deep learning approach based on deep artificial neural network was employed [18]. Results indicated that this deep learning approach outperformed support vector machine and logistic regression by 4.3% and 8.6% respectively.…”

“…Existing works [14]- [18] possessed a common idea of analyzing the optimal feature vector from the dataset. Taking the review articles [19], [20] into account, to the best of our knowledge, there has no consideration on the prediction of students' performance under shadow education environment, that is school tutoring and family tutoring.…”

“…Also, in general, there is room for improvement in the prediction accuracy. A recent work [18] using deep learning was suggested an improvement of accuracy by 4.3% compared to support vector machine with traditional kernel function. The improvement may become insignificant if customized kernel or multiple kernel learning approach is adopted.…”

Artificial intelligence and deep learning in educational technology research and practice

“…The AI market size is expected to reach $390.9 billion by 2025 (Grand View Research, 2019) through a wide array of applications including natural language processing, intelligent decision making and robotic automation. As a branch of AI, deep learning techniques have also achieved tremendous success in various areas (Rizvi, Rienties, & Khoja, 2019;Waheed et al, 2020) including personalized recommendation, computer vision, linguistics and bioinformatics. As COVID-19 has compelled schools to close, the outbreak of COVID-19 dramatically increased the usage of online learning applications embedded with AI and deep learning algorithms to support remote learning (Wang, 2020).…”