A Novel Chaotic Optimized Boost Long Short-Term Memory (COB-LSTM) Model for Students Academic Performance Prediction in Educational Sectors
Keywords:
Educational Data Mining (EDM), Students’ Performance Prediction, Chaotic Henry Gas Solubility (CHGS) optimization, Boost integrated Deep Long Short Term Memory (Bo-LSTM), UCI Public DatasetAbstract
Due to the vast amount of data in educational databases, predicting student performance is a significant and complex task. Educational Data Mining (EDM) is used to do this, which creates techniques for locating data produced from educational settings. Understanding students’ performance and their learning environment is accomplished using these techniques. Educational institutions sometimes wonder to examine that how many students will pass or fail in order to make the appropriate plans. It has been noted that in earlier studies many researchers focused on choosing the best algorithm for proper classification and neglect looking for solutions to issues that arise during the data mining and analysis phases, including high dimensionality, unbalanced classes, and sorting error. Therefore, the proposed work aims to develop a novel framework, referred to as, Chaotic Optimized Boost Long Short Term Memory (COB-LSTM) for students’ academic performance prediction and classification. By using the public UCI education training dataset, a novel method for forecasting and analyzing students' educational achievement is developed in this work. After obtaining a dataset, the Chaotic Henry Gas Solubility (CHGS) optimization technique is used to choose the best traits for a classification that is accurate and low in data dimensionality. Then, a classification method based on the hybrid Boost integrated Deep Long Short Term Memory (Bo-LSTM) is used to accurately predict student performance. Several metrics and datasets have been employed in this study to test and evaluate the performance and results of the proposed COB-LSTM model.
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N. R. Yadav and S. S. Deshmukh, "Prediction of Student Performance Using Machine Learning Techniques: A Review," in International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022), 2023, pp. 735-741.
G. Phatai and T. Luangrungruang, "A Comparative Study of Hybrid Neural Network with Metaheuristics for Student Performance Classification," in 2023 11th International Conference on Information and Education Technology (ICIET), 2023, pp. 448-452.
I. Babu, R. MathuSoothana, and S. Kumar, "Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis," Intelligent Automation & Soft Computing, vol. 36, 2023.
D. Yewale, S. Vijayaragavan, and V. Bairagi, "An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning," International Journal of Advanced Computer Science and Applications, vol. 14, 2023.
E. Evangelista, "An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance," International Journal of Emerging Technologies in Learning (Online), vol. 18, p. 150, 2023.
I. Issah, O. Appiah, P. Appiahene, and F. Inusah, "A systematic review of the literature on machine learning application of determining the attributes influencing academic performance," Decision Analytics Journal, p. 100204, 2023.
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," Education and Information Technologies, pp. 1-30, 2023.
Z. Wang, W. Yan, C. Zeng, Y. Tian, and S. Dong, "A unified interpretable intelligent learning diagnosis framework for learning performance prediction in intelligent tutoring systems," International Journal of Intelligent Systems, vol. 2023, 2023.
S. Batool, J. Rashid, M. W. Nisar, J. Kim, H.-Y. Kwon, and A. Hussain, "Educational data mining to predict students' academic performance: A survey study," Education and Information Technologies, vol. 28, pp. 905-971, 2023.
P. Guleria and M. Sood, "Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling," Education and Information Technologies, vol. 28, pp. 1081-1116, 2023.
T. Cardona, E. A. Cudney, R. Hoerl, and J. Snyder, "Data mining and machine learning retention models in higher education," Journal of College Student Retention: Research, Theory & Practice, vol. 25, pp. 51-75, 2023.
G. Shen, S. Yang, Z. Huang, Y. Yu, and X. Li, "The prediction of programming performance using student profiles," Education and Information Technologies, vol. 28, pp. 725-740, 2023.
A. Emerson, W. Min, R. Azevedo, and J. Lester, "Early prediction of student knowledge in game‐based learning with distributed representations of assessment questions," British Journal of Educational Technology, vol. 54, pp. 40-57, 2023.
M. Alshurideh, B. Al Kurdi, S. A. Salloum, I. Arpaci, and M. Al-Emran, "Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms," Interactive Learning Environments, vol. 31, pp. 1214-1228, 2023.
M. H. B. Roslan and C. J. Chen, "Predicting students’ performance in English and Mathematics using data mining techniques," Education and Information Technologies, vol. 28, pp. 1427-1453, 2023.
K. Karataş, I. Arpaci, and Y. Yildirim, "Predicting the culturally responsive teacher roles with cultural intelligence and self-efficacy using machine learning classification algorithms," Education and Urban Society, vol. 55, pp. 674-697, 2023.
R. Mehdi and M. Nachouki, "A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs," Education and Information Technologies, vol. 28, pp. 2455-2484, 2023.
Y. Teng, J. Zhang, and T. Sun, "Data‐driven decision‐making model based on artificial intelligence in higher education system of colleges and universities," Expert Systems, vol. 40, p. e12820, 2023.
A. B. Bhatia, K. Mittal, R. Mahajan, and P. Whig, "Computational Psychometrics Social Analysis of Learners in Their Learning Behaviour Using AI Algorithms," in AI-Enabled Social Robotics in Human Care Services, ed: IGI Global, 2023, pp. 1-32.
M. Yağcı, "Educational data mining: prediction of students' academic performance using machine learning algorithms," Smart Learning Environments, vol. 9, p. 11, 2022.
S. Hussain and M. Q. Khan, "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, vol. 10, pp. 637-655, 2023/06/01 2023.
D. T. Ha, P. T. T. Loan, C. N. Giap, and N. T. L. Huong, "An empirical study for student academic performance prediction using machine learning techniques," International Journal of Computer Science and Information Security (IJCSIS), vol. 18, pp. 75-82, 2020.
M. Imran, S. Latif, D. Mehmood, and M. S. Shah, "Student academic performance prediction using supervised learning techniques," International Journal of Emerging Technologies in Learning, vol. 14, 2019.
A. Abu Saa, M. Al-Emran, and K. Shaalan, "Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques," Technology, Knowledge and Learning, vol. 24, pp. 567-598, 2019.
N. Tomasevic, N. Gvozdenovic, and S. Vranes, "An overview and comparison of supervised data mining techniques for student exam performance prediction," Computers & education, vol. 143, p. 103676, 2020.
H. A. Mengash, "Using data mining techniques to predict student performance to support decision making in university admission systems," Ieee Access, vol. 8, pp. 55462-55470, 2020.
C. Jalota and R. Agrawal, "Analysis of educational data mining using classification," in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 243-247.
M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, "Systematic ensemble model selection approach for educational data mining," Knowledge-Based Systems, vol. 200, p. 105992, 2020.
B. Albreiki, N. Zaki, and H. Alashwal, "A systematic literature review of student’performance prediction using machine learning techniques," Education Sciences, vol. 11, p. 552, 2021.
H. Waheed, S.-U. Hassan, N. R. Aljohani, J. Hardman, S. Alelyani, and R. Nawaz, "Predicting academic performance of students from VLE big data using deep learning models," Computers in Human behavior, vol. 104, p. 106189, 2020.
S. Shreem and H. Turabieh, "Student’s Performance Prediction using Hybrid Machine Learning Classifiers," Int. J. Comput. Sci. Inf. Secur.(IJCSIS), vol. 19, pp. 87-103, 2021.
Dineshkumar, R., Kalimuthu, M., Deepika, K., & Gopalakrishnan, S. (2022). Engineering education with tool based technical activity (TBTA). Journal of Engineering Education Transformations, 36(2, October), 185–191. https://doi.org/10.16920/jeet/2022/v36i2/22166
Dineshkumar, R., Kalimuthu, M., Deepika, K., & Gopalakrishnan, S. (2022). Engineering education with tool based technical activity (TBTA). Journal of Engineering Education Transformations, 36(2, October), 185–191. https://doi.org/10.16920/jeet/2022/v36i2/22166
Vaqur, M. ., Kumar, R. ., Singh, R. ., Umang, U., Gehlot, A. ., Vaseem Akram, S. ., & Joshi, K. . (2023). Role of Digitalization in Election Voting Through Industry 4.0 Enabling Technologies. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 123–130. https://doi.org/10.17762/ijritcc.v11i2.6136
Elena Petrova, Predictive Analytics for Customer Churn in Telecommunications , Machine Learning Applications Conference Proceedings, Vol 1 2021.
Soundararajan, R., Stanislaus, P. M., Ramasamy, S. G., Dhabliya, D., Deshpande, V., Sehar, S., Bavirisetti, D. P. Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks (2023) Energies, 16 (5), art. no. 2285, .
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