Student’s performance prediction based on an improved multi-view hypergraph neural network

References

• Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
   Web of Science ®Google Scholar
• Arya, D., & Worring, M. (2018). Exploiting relational information in social networks using geometric deep learning on hypergraphs. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, Yokohama, Japan.
   Google Scholar
• Bai, X., Zhang, F., Li, J., Guo, T., Aziz, A., Jin, A., & Xia, F. (2021). Educational big data: Predictions, applications and challenges. Big Data Research, 26, 100270. https://doi.org/10.1016/j.bdr.2021.100270
   Web of Science ®Google Scholar
• Bernhard, S., John, P., & Thomas, H. (2006). Learning with hypergraphs: Clustering, classification, and embedding. Advances in Neural Information Processing Systems, 1601–1608.
   Google Scholar
• Bradley, V. M. (2021). Learning Management System (LMS) use with online instruction. International Journal of Technology in Education, 4(1), 68–92. https://doi.org/10.46328/ijte.36
   Web of Science ®Google Scholar
• Cao, Y., Gao, J., Lian, D., Rong, Z., Shi, J., Wang, Q., Wu, Y., Yao, H., & Zhou, T. (2018). Orderliness predicts academic performance: Behavioural analysis on campus lifestyle. Journal of the Royal Society Interface, 15(146), 20180210. https://doi.org/10.1098/rsif.2018.0210
   PubMed Web of Science ®Google Scholar
• Chang, J., Chen, Y., Qi, L., & Yan, H. (2020). Hypergraph clustering using a new Laplacian tensor with applications in image processing. SIAM Journal on Imaging Sciences, 13(3), 1157–1178. https://doi.org/10.1137/19M1291601
   Web of Science ®Google Scholar
• Dagiliūtė, R., Liobikienė, G., & Minelgaitė, A. (2018). Sustainability at universities: Students’ perceptions from green and non-green universities. Journal of Cleaner Production, 181, 473–482. https://doi.org/10.1016/j.jclepro.2018.01.213
   Web of Science ®Google Scholar
• Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems, 29.
   Google Scholar
• Feng, Y., You, H., Zhang, Z., Ji, R., & Gao, Y. (2019). Hypergraph neural networks. Proceedings of the AAAI conference on artificial intelligence, Honolulu, Hawaii, USA.
   Google Scholar
• Gaftandzhieva, S., Talukder, A., Gohain, N., Hussain, S., Theodorou, P., Salal, Y. K., & Doneva, R. (2022). Exploring online activities to predict the final grade of student. Mathematics, 10(20), 3758. https://doi.org/10.3390/math10203758
   Web of Science ®Google Scholar
• Garg, S., Ahmad, A., Madsen, D. Ø., & Sohail, S. S. (2023). Sustainable behavior with respect to managing E-wastes: Factors influencing E-waste management among young consumers. International Journal of Environmental Research and Public Health, 20(1), 801. https://doi.org/10.3390/ijerph20010801
   PubMed Web of Science ®Google Scholar
• Hershner, S. (2020). Sleep and academic performance: Measuring the impact of sleep. Current Opinion in Behavioral Sciences, 33, 51–56. https://doi.org/10.1016/j.cobeha.2019.11.009
   Web of Science ®Google Scholar
• Hussain, S. (2015). Educational data mining using R programming and R studio. Journal of Applied and Fundamental Sciences, 1(1), 45.
   Google Scholar
• Hussain, S. (2017). Survey on current trends and techniques of data mining research. London Journal of Research in Computer Science and Technology, 17(1), 11.
   Google Scholar
• Hussain, S., & Hazarika, G. (2014). Educational data mining model using rattle. International Journal of Advanced Computer Science and Applications, 5(6). https://doi.org/10.14569/IJACSA.2014.050605
   Google Scholar
• Kasim, N. N. M., & Khalid, F. (2016). Choosing the right learning management system (LMS) for the higher education institution context: A systematic review. International Journal of Emerging Technologies in Learning (iJET), 11(6), 55. https://doi.org/10.3991/ijet.v11i06.5644
   Web of Science ®Google Scholar
• Kipf, T. N., & Welling, M. (2016, arXiv preprint arXiv:1609.02907). Semi-supervised classification with graph convolutional networks.
   Google Scholar
• Kreuzer, C., Weber, S., Off, M., Hackenberg, T., & Birk, C. (2019). Shedding light on realized sustainable consumption behavior and perceived barriers of young adults for creating stimulating teaching–learning situations. Sustainability, 11(9), 2587. https://doi.org/10.3390/su11092587
   Web of Science ®Google Scholar
• Li, Y., & Li, D. (2020). University students’ behavior characteristics analysis and prediction method based on combined data mining model. Proceedings of the 2020 the 3rd International Conference on Computers in Management and Business, Tokyo, Japan.
   Google Scholar
• Li, Y., Zhang, S., Cheng, D., He, W., Wen, G., & Xie, Q. (2017). Spectral clustering based on hypergraph and self-re-presentation. Multimedia Tools and Applications, 76(16), 17559–17576. https://doi.org/10.1007/s11042-016-4131-6
   Web of Science ®Google Scholar
• Li, L., Zhang, L., & Kaur, A. (2022). The relationship between physical activity and academic achievement in multimodal environment using computational analysis. Computational Intelligence and Neuroscience, 2022, 1–10. https://doi.org/10.1155/2022/9418004
   Web of Science ®Google Scholar
• Li, M., Zhang, Y., Li, X., Cai, L., & Yin, B. (2022). Multi-view hypergraph neural networks for student academic performance prediction. Engineering Applications of Artificial Intelligence, 114, 105174. https://doi.org/10.1016/j.engappai.2022.105174
   Web of Science ®Google Scholar
• Mak van der Vossen, M. C., van Mook, W. N., Kors, J. M., van Wieringen, W. N., Peerdeman, S. M., Croiset, G., & Kusurkar, R. A. (2016). Distinguishing three unprofessional behavior profiles of medical students using latent class analysis. Academic Medicine, 91(9), 1276–1283. https://doi.org/10.1097/ACM.0000000000001206
   Web of Science ®Google Scholar
• Ma, W., Lin, X., Lou, J., Liu, Y., Tang, W., & Bao, Z. (2023). The impact of students’ cellphone-use and self-control on academic performance in traditional classroom. Asia Pacific Education Review, 24(4), 1–8. https://doi.org/10.1007/s12564-023-09824-6
   Web of Science ®Google Scholar
• McGinness, H. T., Caldwell, P. H., Gunasekera, H., & Scott, K. M. (2022). ‘Every human interaction requires a bit of give and take’: Medical students’ approaches to pursuing feedback in the clinical setting. Teaching and Learning in Medicine, 35(4), 1–11. https://doi.org/10.1080/10401334.2022.2084401
   Web of Science ®Google Scholar
• Namoun, A., & Alshanqiti, A. (2020). Predicting student performance using data mining and learning analytics techniques: A systematic literature review. Applied Sciences, 11(1), 237. https://doi.org/10.3390/app11010237
   Google Scholar
• Nong, L., Wang, J., Lin, J., Qiu, H., Zheng, L., & Zhang, W. (2021). Hypergraph wavelet neural networks for 3D object classification. Neurocomputing, 463, 580–595. https://doi.org/10.1016/j.neucom.2021.08.006
   Web of Science ®Google Scholar
• Serra, R., Kiekens, G., Vanderlinden, J., Vrieze, E., Auerbach, R. P., Benjet, C., Claes, L., Cuijpers, P., Demyttenaere, K., Ebert, D. D., Tarsitani, L., Green, J. G., Kessler, R. C., Nock, M. K., Mortier, P., & Bruffaerts, R. (2020). Binge eating and purging in first‐year college students: Prevalence, psychiatric comorbidity, and academic performance. International Journal of Eating Disorders, 53(3), 339–348. https://doi.org/10.1002/eat.23211
   Web of Science ®Google Scholar
• Strizek, J., Uhl, A., Schaub, M., & Malischnig, D. (2021). Alcohol and cigarette use among adolescents and young adults in Austria from 2004–2020: Patterns of change and associations with socioeconomic variables. International Journal of Environmental Research and Public Health, 18(24), 13080. https://doi.org/10.3390/ijerph182413080
   Web of Science ®Google Scholar
• Valem, L. P., & Pedronette, D. C. G. (2022). Person re-ID through unsupervised hypergraph rank selection and fusion. Image and Vision Computing, 123, 104473. https://doi.org/10.1016/j.imavis.2022.104473
   Web of Science ®Google Scholar
• Valladares, M., Durán, E., Matheus, A., Durán-Agüero, S., Obregón, A. M., & Ramírez-Tagle, R. (2016). Association between eating behavior and academic performance in university students. Journal of the American College of Nutrition, 35(8), 699–703. https://doi.org/10.1080/07315724.2016.1157526
   PubMed Web of Science ®Google Scholar
• Wu, H., & Ng, M. K. (2022). Hypergraph convolution on nodes-hyperedges network for semi-supervised node classification. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(4), 1–19. https://doi.org/10.1145/3494567
   Web of Science ®Google Scholar
• Yun, S., Jeong, M., Kim, R., Kang, J., & Kim, H. J. (2019). Graph transformer networks. Advances in Neural Information Processing Systems, 32.
   Google Scholar
• Zhang, Y., Zhang, H., Xiao, L., Bai, Y., Calhoun, V. D., & Wang, Y.-P. (2022). Multi-modal imaging genetics data fusion via a hypergraph-based manifold regularization: Application to schizophrenia study. IEEE Transactions on Medical Imaging, 41(9), 2263–2272. https://doi.org/10.1109/TMI.2022.3161828
   Web of Science ®Google Scholar