Department of Computer Science and Engineering, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University)Pune, India
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B. H. Kim, E. Vizitei, and V. Ganapathi, “GritNet: Student performance prediction with deep learning,” Proc. 11th Int. Conf. Educ. Data Mining, EDM 2018, 2018.KimB. H.ViziteiE.GanapathiV.“GritNet: Student performance prediction with deep learning,”Proc. 11th Int. Conf. Educ. Data Mining, EDM 20182018Search in Google Scholar
N. Aslam, I. U. Khan, L. H. Alamri, and R. S. Almuslim, “An Improved Early Student’s Performance Prediction Using Deep Learning,” Int. J. Emerg. Technol. Learn., vol. 16, no. 12, pp. 108–122, 2021, doi: 10.3991/ijet.v16i12.20699.AslamN.KhanI. U.AlamriL. H.AlmuslimR. S.“An Improved Early Student’s Performance Prediction Using Deep Learning,”Int. J. Emerg. Technol. Learn.1612108122202110.3991/ijet.v16i12.20699Open DOISearch in Google Scholar
S. Li and T. Liu, “Performance Prediction for Higher Education Students Using Deep Learning,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/9958203.LiS.LiuT.“Performance Prediction for Higher Education Students Using Deep Learning,”Complexity2021202110.1155/2021/9958203Open DOISearch in Google Scholar
S. Hussain and M. Q. Khan, “Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning,” Ann. Data Sci., vol. 10, no. 3, pp. 637–655, 2023, doi: 10.1007/s40745-021-00341-0.HussainS.KhanM. Q.“Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning,”Ann. Data Sci.103637655202310.1007/s40745-021-00341-0Open DOISearch in Google Scholar
F. Ouyang, M. Wu, L. Zheng, L. Zhang, and P. Jiao, “Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course,” Int. J. Educ. Technol. High. Educ., vol. 20, no. 1, pp. 1–23, 2023, doi: 10.1186/s41239-022-00372-4.OuyangF.WuM.ZhengL.ZhangL.JiaoP.“Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course,”Int. J. Educ. Technol. High. Educ.201123202310.1186/s41239-022-00372-4Open DOISearch in Google Scholar
M. Arashpour et al., “Predicting individual learning performance using machine-learning hybridized with the teaching-learning-based optimization,” Comput. Appl. Eng. Educ., vol. 31, no. 1, pp. 83–99, 2023, doi: 10.1002/cae.22572.ArashpourM.“Predicting individual learning performance using machine-learning hybridized with the teaching-learning-based optimization,”Comput. Appl. Eng. Educ.3118399202310.1002/cae.22572Open DOISearch in Google Scholar
A. S. Mohammad, M. T. S. Al-Kaltakchi, J. Alshehabi Al-Ani, and J. A. Chambers, “Comprehensive Evaluations of Student Performance Estimation via Machine Learning,” Mathematics, vol. 11, no. 14, pp. 1–16, 2023, doi: 10.3390/math11143153.MohammadA. S.Al-KaltakchiM. T. S.Alshehabi Al-AniJ.ChambersJ. A.“Comprehensive Evaluations of Student Performance Estimation via Machine Learning,”Mathematics1114116202310.3390/math11143153Open DOISearch in Google Scholar
F. A. Al-azazi and M. Ghurab, “ANN-LSTM: A deep learning model for early student performance prediction in MOOC,” Heliyon, vol. 9, no. 4, p. e15382, 2023, doi: 10.1016/j.heliyon.2023.e15382.Al-azaziF. A.GhurabM.“ANN-LSTM: A deep learning model for early student performance prediction in MOOC,”Heliyon94e15382202310.1016/j.heliyon.2023.e15382Open DOISearch in Google Scholar
A. Kukkar, R. Mohana, A. Sharma, and A. Nayyar, “Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms,” Educ. Inf. Technol., vol. 28, no. 8, pp. 9655–9684, 2023, doi: 10.1007/s10639-022-11573-9.KukkarA.MohanaR.SharmaA.NayyarA.“Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms,”Educ. Inf. Technol.28896559684202310.1007/s10639-022-11573-9Open DOISearch in Google Scholar
Rahul and R. Katarya, “Deep auto encoder based on a transient search capsule network for student performance prediction,” Multimed. Tools Appl., vol. 82, no. 15, pp. 23427–23451, 2023, doi: 10.1007/s11042-022-14083-5.RahulKataryaR.“Deep auto encoder based on a transient search capsule network for student performance prediction,”Multimed. Tools Appl.82152342723451202310.1007/s11042-022-14083-5Open DOISearch in Google Scholar
Y. Chen and Y. Mei, “Competency Model: A Study on the Cultivation of College Students’ Innovation and Entrepreneurship Ability,” HighTech Innov. J., vol. 4, no. 4, pp. 821–828, 2023.ChenY.MeiY.“Competency Model: A Study on the Cultivation of College Students’ Innovation and Entrepreneurship Ability,”HighTech Innov. J.448218282023Search in Google Scholar
N. Salybekova, S. Abdimalik, G. Issayev, G. Khalikova, A. Berdenkulova, and K. Bakirova, “E-Learning Adoption: Designing a Network-Based Educational and Methodological Course on” Humans and Their Health,” Emerg. Sci. J., vol. 7, no. 6, pp. 2097–2119, 2023.SalybekovaN.AbdimalikS.IssayevG.KhalikovaG.BerdenkulovaA.BakirovaK.“E-Learning Adoption: Designing a Network-Based Educational and Methodological Course on” Humans and Their Health,”Emerg. Sci. J.76209721192023Search in Google Scholar
M. S. Hasibuan, R. Z. A. Aziz, D. A. Dewi, T. B. Kurniawan, and N. A. Syafira, “Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach,” HighTech Innov. J., vol. 4, no. 4, pp. 811–820, 2023.HasibuanM. S.AzizR. Z. A.DewiD. A.KurniawanT. B.SyafiraN. A.“Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach,”HighTech Innov. J.448118202023Search in Google Scholar
H. He, Y. Bai, E. A. Garcia, and S. Li, “ADASYN: Adaptive synthetic sampling approach for imbalanced learning,” Proc. Int. Jt. Conf. Neural Networks, no. 3, pp. 1322–1328, 2008, doi: 10.1109/IJCNN.2008.4633969.HeH.BaiY.GarciaE. A.LiS.“ADASYN: Adaptive synthetic sampling approach for imbalanced learning,”Proc. Int. Jt. Conf. Neural Networks313221328200810.1109/IJCNN.2008.4633969Open DOISearch in Google Scholar
M. Taye, “Theoretical Understanding of Convolutional Neural Network :,” Computation, vol. 11, 2023.TayeM.“Theoretical Understanding of Convolutional Neural Network :,”Computation112023Search in Google Scholar
M. Arya and G. Hanumat Sastry, Effective LSTM Neural Network with Adam Optimizer for Improving Frost Prediction in Agriculture Data Stream, vol. 1761 CCIS. Springer Nature Switzerland, 2023.AryaM.Hanumat SastryG.Effective LSTM Neural Network with Adam Optimizer for Improving Frost Prediction in Agriculture Data Stream, vol. 1761 CCISSpringerNature Switzerland2023Search in Google Scholar
J. Billa, “Dropout approaches for LSTM based speech recognition systems,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2018-April, pp. 5879–5883, 2018, doi: 10.1109/ICASSP.2018.8462544.BillaJ.“Dropout approaches for LSTM based speech recognition systems,”ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc.2018April58795883201810.1109/ICASSP.2018.8462544Open DOISearch in Google Scholar
A. Graves, S. Fernández, and J. Schmidhuber, “Bidirectional LSTM networks for improved phoneme classification and recognition,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3697 LNCS, pp. 799–804, 2005, doi: 10.1007/11550907_126.GravesA.FernándezS.SchmidhuberJ.“Bidirectional LSTM networks for improved phoneme classification and recognition,”Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)3697LNCS799804200510.1007/11550907_126Open DOISearch in Google Scholar