Open Access

A CNN–LSTM-based deep learning model for early prediction of student’s performance

, , , ,  and   
Dec 02, 2024

Cite
Download Cover

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 2018 2018 Search 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. 16 12 108 122 2021 10.3991/ijet.v16i12.20699 Open 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,” Complexity 2021 2021 10.1155/2021/9958203 Open 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. 10 3 637 655 2023 10.1007/s40745-021-00341-0 Open 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. 20 1 1 23 2023 10.1186/s41239-022-00372-4 Open 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. 31 1 83 99 2023 10.1002/cae.22572 Open 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,” Mathematics 11 14 1 16 2023 10.3390/math11143153 Open 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,” Heliyon 9 4 e15382 2023 10.1016/j.heliyon.2023.e15382 Open 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. 28 8 9655 9684 2023 10.1007/s10639-022-11573-9 Open 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. Rahul KataryaR. “Deep auto encoder based on a transient search capsule network for student performance prediction,” Multimed. Tools Appl. 82 15 23427 23451 2023 10.1007/s11042-022-14083-5 Open 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. 4 4 821 828 2023 Search 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. 7 6 2097 2119 2023 Search 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. 4 4 811 820 2023 Search 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 Networks 3 1322 1328 2008 10.1109/IJCNN.2008.4633969 Open DOISearch in Google Scholar

M. Taye, “Theoretical Understanding of Convolutional Neural Network :,” Computation, vol. 11, 2023. TayeM. “Theoretical Understanding of Convolutional Neural Network :,” Computation 11 2023 Search 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 CCIS Springer Nature Switzerland 2023 Search 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. 2018 April 5879 5883 2018 10.1109/ICASSP.2018.8462544 Open 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) 3697 LNCS 799 804 2005 10.1007/11550907_126 Open DOISearch in Google Scholar

Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Engineering, Introductions and Overviews, Engineering, other