The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index.
Predicting academic performance of students from VLE big data using deep learning models
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Machine learning models such as deep neural networks predicting learning outcomes [23] and transformers facilitating student knowledge tracing [19] are deeply dependent on the availability of large amounts of data.”
From Federated Learning Analytics: Investigating the Privacy-Performance Trade-Off in Machine Learning for Educational Analytics (2024) · cites this paper as [23]
In the citing paper
“Since Waheed et al. [23] extensively describe the features they engineered, we were able to reproduce these features and use them as input for OULAD classification.”
From Federated Learning Analytics: Investigating the Privacy-Performance Trade-Off in Machine Learning for Educational Analytics (2024) · cites this paper as [23]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Principal component analysis: A review and recent developments
Jolliffe, I. T., & Cadima, J. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0202 · Cited by 10,117
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865
Penetrating the Fog: Analytics in Learning and Education.
Siemens, G., & Long, P. (2011) · Adelaide Research & Scholarship (AR&S) (University of Adelaide) · Cited by 1,752
Educational Data Mining and Learning Analytics
Baker, R. S., & Inventado, P. S. (2014) · Learning Analytics · doi:10.1007/978-1-4614-3305-7_4 · Cited by 1,029
Course signals at Purdue: using learning analytics to increase student success
Arnold, K. E., & Pistilli, M. D. (2012) · Proceedings of the 2nd International Conference on Learning Analytics and Knowledge · doi:10.1145/2330601.2330666 · Cited by 987
A Review on Predicting Student's Performance Using Data Mining Techniques
Shahiri, A. M., Husain, W., & Rashid, N. A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.157 · Cited by 840
The current landscape of learning analytics in higher education
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.027 · Cited by 766 · cited 1×
“The progression of the accumulated educational data has stimulated the emergence of several research communities, such as learning analytics to predict learners’ behavior and providing indicators for optimized policy formulations (Azcona & Smeaton, 2017; Viberg et al., 2018; Capuano & Toti 2019).”
Background
Cited by
Artificial Intelligence in Education: A Systematic Literature Review
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124167 · Cited by 977
Yağcı, M. (2022) · Smart Learning Environments · doi:10.1186/s40561-022-00192-z · Cited by 698
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00372-4 · Cited by 387
Dimitriadou, E., & Lanitis, A. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00231-3 · Cited by 370
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139
Metadata
- Authors
- Waheed, H., Hassan, S.-U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 104
- Pages
- 106189
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2983382509
- Cited by
- 573
- References
- 85
- FWCI
- 47.65
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Predicting academic performance of students from VLE big data using deep learning models. https://app.cochat.ai/papers/10.1016/j.chb.2019.106189
- The paper
- Waheed, H., Hassan, S.-U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2019.106189
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
