Education 4.0 using artificial intelligence for students performance analysis

Abstract

Nowadays, predicting students' performance is one of the most specific topics for learning environments, such as universities and schools, since it leads to the development of effective mechanisms that can enhance academic outcomes and avoid destruction. In education 4.0, Artificial Intelligence (AI) can play a key role in identifying new factors in students' performance and implementing personalized learning, answering routine student questions, using learning analytics, and predictive modeling. It is a new challenge to redefine education 4.0 to recognize the creative and innovative intelligent students, and it is difficult to determine students' outcomes. Hence, in this paper, Hybridized Deep Neural Network (HDNN) to predict student performance in Education 4.0. The proposed HDNN method is utilized to determine the dynamics that likely influence the student's performance. The deep neural network monitors predict, and evaluate students' performance in an education 4.0 environment. The findings show that the proposed HDNN method achieved better prediction accuracy when compared to other popular methods.

Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

“…Some of the examples are presented in (Abdullah et al, 2021 ; Cerezo et al, 2017 ; Lu et al, 2018 ; Mansouri et al, 2021 ; Shayan & van Zaanen, 2019 ), authors used students’ behaviors logs containing their interactions with the online content to assess their performance, while Ayouni et al ( 2021 ) and Hussain et al ( 2018 ) used it to measure students’ levels of engagement. (Chen & Cui, 2020 ; Chen et al, 2020 ; Dass et al, 2021 ; Macarini et al, 2019 ) utilized features related to time spent on online materials to identify at-risk of failing students or the ones who are most likely to dropout. However, other authors (Adnan et al, 2021 ; Goel & Goyal, 2020 ; Karalar et al, 2021 ; Waheed et al, 2020 ; Yu et al, 2019 ) utilized behavioral data compromised in students' clickstreams data.…”

“…As an example, Neha et al ( 2021 ) proposed a deep neural network for evaluating student performance assessments, meanwhile Ayouni et al ( 2021 ) used an ANN to predict students’ engagement in an online environment. In (Chen et al, 2020 ), researchers employed a hybridized deep neural network to identify students at risk early in the exams. (Chen & Cui, 2020 ; Dias et al, 2020 ; Mubarak et al, 2021 ) utilized the recurrent neural network named long short-term memory (LSTM) to predict course performance, evaluate the quality of interactions and students’ involvement, and predict students’ weekly performance, respectively.…”

Revolutionizing Education: Empowering Disaster Preparedness through Innovative Smart Teaching with e-Modules on FlipCreator Pro Platform

“…Therefore, a supporting application is needed to cover all the content needed, so that the effectiveness of learning on disaster mitigation material can be fulfilled. The Smart Teaching approach, utilizing technology and artificial intelligence, effectively enhances student engagement (Chen et al, 2020;Dimitriadou & Lanitis, 2023). By delivering interactive content through applications and online platforms, teachers create a compelling learning experience.…”