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

By Hajra Waheed, Saeed-Ul Hassan, Naif R. Aljohani, Julie Hardman, Salem Alelyani, Raheel Nawaz. Computers in Human Behavior, 2020

476 citations recorded by the source corpus.

Abstract The abundance of accessible educational data, supported by the technology-enhanced learning platforms, provides opportunities to mine learning behavior of students, addressing their issues, optimizing the educational environment, and enabling data-driven decision making. Virtual learning environments complement the learning analytics paradigm by effectively providing datasets for analysing and reporting the learning process of students and its reflection and contribution in their respective performances. This study deploys a deep artificial neural network on a set of unique handcrafted features, extracted from the virtual learning environments clickstream data, to predict at-risk students providing measures for early intervention of such cases. The results show the proposed model to achieve a classification accuracy of 84%–93%. We show that a deep artificial neural network outperforms the baseline logistic regression and support vector machine models. While logistic regression achieves an accuracy of 79.82%–85.60%, the support vector machine achieves 79.95%–89.14%. Aligned with the existing studies - our findings demonstrate the inclusion of legacy data and assessment-related data to impact the model significantly. Students interested in accessing the content of the previous lectures are observed to demonstrate better performance. The study intends to assist institutes in formulating a necessary framework for pedagogical support, facilitating higher education decision-making process towards sustainable education.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/db63aa5fc7024c7ffc0f602f9b07ad59782666a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Andreas Lanitis
2. Pengcheng Jiao
3. Luyi Zheng
4. Ouyang Fan
5. M. Yağcı
6. Eleni Dimitriadou

In Computer Science Applications

1. Mian Wu
2. Pengcheng Jiao
3. Luyi Zheng
4. Liyin Zhang
5. Ouyang Fan
6. M. Yağcı

Citations in the corpus

• Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course (2023, methodology): “Deep artificial neural network (ANN) was deployed to predict at-risk students with early intervention using the unique handcrafted features and data extracted from the virtual learning environments (Waheed et al., 2020).”
• A critical evaluation, challenges, and future perspectives of using artificial intelligence and emerging technologies in smart classrooms (2023, background): “Waheed et al. (2020), propose a system to predict the students’ academic performance in a virtual learning environment.”
• Educational data mining: prediction of students' academic performance using machine learning algorithms (2022, methodology): “Learning analytics has gained a new dimension through the use of EDM (Waheed et al., 2020).”