Advancing Student Learning Assessment: A Novel Hybrid Neural Network Approach Integrating GRU and LSTM Architectures.
Keywords:
Deep Learning, LSTM, GRU, Hybrid Neural Network, Student Performance Assessment, Exploratory Data AnalysisAbstract
Timely and accurate assessment of student learning is essential for the effective functioning of educational institutions, guiding program development and instructional strategies. This study introduces an innovative method employing a Hybrid Neural Network that integrates Gated Recurrent Units (GRU) and Long Short-Term Memories (LSTM) architectures. This fusion forms a unique Hybrid Neural Network, capitalizing on the distinctive features of GRU and LSTM to enhance the reliability and predictability of student assessments. The research not only propels the field of student performance evaluation but also unveils a ground-breaking application for the LSTM-GRU Hybrid Neural Network design. Performance metrics, such as Mean Squared Error (MSE) and Loss, were meticulously analyzed. The Hybrid Neural Network demonstrated superior performance, boasting an MSE of 0.236 and a Validation MSE of 0.285. Furthermore, the Loss was 0.236, and the Validation Loss was 0.285. Comparative evaluations against conventional LSTM and GRU models underscored the significant performance enhancements achieved by the Hybrid Neural Network. In evaluating the effectiveness of past approaches, our study unveiled that the proposed Hybrid Neural Network consistently outperformed in terms of MSE, suggesting unparalleled performance compared to existing studies. This research contributes significantly to the evolving landscape of educational assessment methodologies, highlighting the transformative potential of Hybrid Neural Networks for elevating evaluation accuracy and predictive capabilities.
Our findings advocate for the widespread adoption of this innovative approach in educational institutions, paving the way for improved student assessment mechanisms. As institutions strive for enhanced learning outcomes, the proposed Hybrid Neural Network stands as a beacon for advancing the frontier of educational assessment technology.
Downloads
References
M. Bouhlal, K. Aarika, R. AitAbdelouahid, S. Elfilali, and E. Benlahmar, “Emotions recognition as innovative tool for improving students’ performance and learning approaches”, Procedia Comput. Sci., vol. 175, pp. 597–602, 2020, doi: 10.1016/j.procs.2020.07.086.
V. Matzavela and E. Alepis, “Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments”, Comput. Educ. Artif. Intell., vol. 2, p. 100035, 2021, doi: 10.1016/j.caeai.2021.100035.
P. Bhardwaj, P. K. Gupta, H. Panwar, M. K. Siddiqui, R. Morales-Menendez, and A. Bhaik, “Application of Deep Learning on Student Engagement in e-learning environments”, Comput. Electr. Eng., vol. 93, no. April, p. 107277, 2021, doi: 10.1016/j.compeleceng.2021.107277.
S. Oeda and D. Shimizu, “Verification of usefulness of student modeling with real educational data using convex factorization machines”, Procedia Comput. Sci., vol. 192, pp. 804–811, 2021, doi: 10.1016/j.procs.2021.08.083.
R. Ahuja and A. Banga, “Mental stress detection in University students using machine learning algorithms”, Procedia Comput. Sci., vol. 152, pp. 349–353, 2019, doi: 10.1016/j.procs.2019.05.007.
X. Niu, “Deep-Learning-Guided Student Intelligent Classroom Management System”, Appl. Bionics Biomech., vol. 2022, 2022, doi: 10.1155/2022/1961631.
Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors” , vol. 2022, pp. 1–5, 2022.
Y. Tan, “Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In Management”, Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/3961910.
Y. A. Alsariera, Y. Baashar, G. Alkawsi, A. Mustafa, A. A. Alkahtani, and N. Ali, “Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance” ,vol. 2022, 2022.
Y. Zhang, T. Liu, and B. Ru, “Effect Evaluation and Student Behavior Design Method of Moral Education in Colleges and Universities under the Environment of Deep Learning”, Sci. Program., vol. 2022, 2022, doi: 10.1155/2022/5779130.
J. Waldeyer et al., “A moderated mediation analysis of conscientiousness, time management strategies, effort regulation strategies, and University students’ performance” , Learn. Individ. Differ, vol. 100, no. July, p. 102228, 2022, doi: 10.1016/j.lindif.2022.102228.
T. T. Mai, M. Bezbradica, and M. Crane, “Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data”, Future. Gener. Comput. Syst., vol. 127, pp. 42–55, 2022, doi: 10.1016/j.future.2021.08.026.
R. Alshabandar, A. Hussain, R. Keight, and W. Khan, “Students Performance Prediction in Online Courses Using Machine Learning Algorithms”, Proceedings of Joint Conference of Neural Networks, vol. 02, no. 11, pp. 74–79, 2020, doi : 10.1109/IJCNN48605.2020.9207196.
F. D. Pereira, E. H. T. Oliveira, D. Fernandes, and A. Cristea, “Early performance prediction for CS1 course students using a combination of machine learning and an evolutionary algorithm”, Proc. - IEEE 19th Int. Conf. Adv. Learn. Technol. ICALT 2019, vol. 2161–377X, pp. 183–184, 2019, doi: 10.1109/ICALT.2019.00066.
M. R. Rimadana, S. S. Kusumawardani, P. I. Santosa, and M. S. F. Erwianda, “Predicting Student Academic Performance using Machine Learning and Time Management Skill Data”, 2019 2nd Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2019, pp. 511–515, 2019, doi: 10.1109/ISRITI48646.2019.9034585.
L. M. Abu Zohair, “Prediction of Student’s performance by modelling small dataset size”, Int. J. Educ. Technol. High. Educ., vol. 16, no. 1, 2019, doi: 10.1186/s41239-019-0160-3.
K. Adil and A. Ahmed, “Machine Learning and Deep Learning based Students’ Grades Prediction” pp. 1–18, 2023.
J. Dessain, “Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric”, vol. 199. 2022. doi: 10.1016/j.eswa.2022.116970.
Li and T. Liu, “Performance Prediction for Higher Education Students Using Deep Learning”, Complexity, vol. 2021, 2021, doi: 10.1155/2021/9958203.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.