International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
line decor
  
line decor

 Volume 9, Issue 11 (November 2022), Pages: 84-92

----------------------------------------------

 Original Research Paper

 Student performance prediction with BPSO feature selection and CNN classifier

 Author(s): Safira Begum 1, *, Sunita S. Padmannavar 2

 Affiliation(s):

 1Department of Computer Applications, Visvesvaraya Technological University–RRC, Belgaum, India
 2Department of Computer Applications, KLS Gogte Institute of Technology, Belgaum, India

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-2883-9994

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.11.010

 Abstract:

Educational Data Mining (EDM) is gaining great importance as a new interdisciplinary research field related to some other areas. It is directly related to data mining (DM), the latter being a fundamental part of knowledge discovery in databases (KDD). This data is growing more and more and contains hidden knowledge that could be very useful for users (both teachers and students). It is convenient to identify such knowledge in the form of models, patterns, or any other representation scheme that allows better exploitation of the system. Data mining is revealed as the tool to achieve such discovery, giving rise to EDM. In this complex context, different techniques and learning algorithms are usually used to obtain the best results. Recently educational systems are adopting artificial intelligent systems, especially in the educational context, specific areas for extracting relevant information, such as EDM, which integrates numerous techniques that support the capture, processing, and analysis of these sets of records. The main technique associated with EDM is Machine Learning, which has been used for decades in data processing in different contexts, but with the advent of Big Data, there was an intensification in the application of this technique to extract relevant information from a huge amount of data. This paper proposes the student performance prediction using CNN (Convolution Neural Network) and BPSO (Binary Particle Swarm Optimization) based feature selection method. In this study, classifiers are made for 2-class and 5-class predictions. The proposed system claims an outperforming accuracy of 96.6% with various previous research works as well as found that the majority of attributes related to school activities as compared to data on demographic and socioeconomic characteristics.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Binary particle swarm optimization, Convolution neural network, Data mining, Deep learning, Educational data mining

 Article History: Received 2 April 2022, Received in revised form 18 June 2022, Accepted 29 July 2022

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Begum S and Padmannavar SS (2022). Student performance prediction with BPSO feature selection and CNN classifier. International Journal of Advanced and Applied Sciences, 9(11): 84-92

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 

----------------------------------------------    

 References (31)

  1. Ajibade SSM, Ahmad NB, and Shamsuddin SM (2018). A data mining approach to predict academic performance of students using ensemble techniques. In the International Conference on Intelligent Systems Design and Applications, Springer, Vellore, India: 749-760. https://doi.org/10.1007/978-3-030-16657-1_70   [Google Scholar]
  2. Andrade TLD, Rigo SJ, and Barbosa JLV (2021). Active methodology, educational data mining and learning analytics: A systematic mapping study. Informatics in Education, 20(2): 171-204.   [Google Scholar]
  3. Athani SS, Kodli SA, Banavasi MN, and Hiremath PS (2017). Student academic performance and social behavior predictor using data mining techniques. In the International Conference on Computing, Communication and Automation, IEEE, Greater Noida, India: 170-174. https://doi.org/10.1109/CCAA.2017.8229794   [Google Scholar]
  4. Bhatia P (2019). Data mining and data warehousing: Principles and practical techniques. Cambridge University Press. Cambridge, UK. https://doi.org/10.1017/9781108635592   [Google Scholar]
  5. Chaudhury P, Mishra S, Tripathy HK, and Kishore B (2016). Enhancing the capabilities of student result prediction system. In the Second International Conference on Information and Communication Technology for Competitive Strategies, Udaipur, India: 1-6. https://doi.org/10.1145/2905055.2905150   [Google Scholar]
  6. Deepika K and Sathyanarayana N (2019). Relief-F and budget tree random forest based feature selection for student academic performance prediction. International Journal of Intelligent Engineering and Systems, 12(1): 30-39. https://doi.org/10.22266/ijies2019.0228.04   [Google Scholar]
  7. Hamoud A (2016). Selection of best decision tree algorithm for prediction and classification of students’ action. American International Journal of Research in Science, Technology, Engineering and Mathematics, 16(1): 26-32.   [Google Scholar]
  8. He T, Liu Y, Yu Y, Zhao Q, and Hu Z (2020). Application of deep convolutional neural network on feature extraction and detection of wood defects. Measurement, 152: 107357. https://doi.org/10.1016/j.measurement.2019.107357   [Google Scholar]
  9. Hussain S, Gaftandzhieva S, Maniruzzaman M, Doneva R, and Muhsin ZF (2021). Regression analysis of student academic performance using deep learning. Education and Information Technologies, 26(1): 783-798. https://doi.org/10.1007/s10639-020-10241-0   [Google Scholar]
  10. Hussain S, Muhsion ZF, Salal YK, Theodorou P, Kurtoglu F, and Hazarika GC (2019). Prediction model on student performance based on internal assessment using deep learning. International Journal of Emerging Technologies in Learning, 14(8): 4-22. https://doi.org/10.3991/ijet.v14i08.10001   [Google Scholar]
  11. Khan A and Ghosh SK (2021). Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Education and Information Technologies, 26(1): 205-240. https://doi.org/10.1007/s10639-020-10230-3   [Google Scholar]
  12. Kriegeskorte N and Golan T (2019). Neural network models and deep learning. Current Biology, 29(7): R231-R236. https://doi.org/10.1016/j.cub.2019.02.034   [Google Scholar] PMid:30939301
  13. Kumar L and Bharti KK (2019). An improved BPSO algorithm for feature selection. In: Khare A, Tiwary U, Sethi I, and Singh N (Eds.), Recent trends in communication, computing, and electronics: 505-513. Volume 524, Springer, Singapore, Singapore. https://doi.org/10.1007/978-981-13-2685-1_48   [Google Scholar]
  14. Ma X and Zhou Z (2018). Student pass rates prediction using optimized support vector machine and decision tree. In the IEEE 8th Annual Computing and Communication Workshop and Conference, IEEE, Las Vegas, USA: 209-215. https://doi.org/10.1109/CCWC.2018.8301756   [Google Scholar]
  15. Malini J (2021). Analysis of factors affecting student performance evaluation using education datamining technique. Turkish Journal of Computer and Mathematics Education, 12(7): 2413-2424.   [Google Scholar]
  16. Mangina E and Psyrra G (2021). Review of learning analytics and educational data mining applications. In the 13th Annual International Conference on Education and New Learning Technologies, Palma, Spain: 949-954. https://doi.org/10.21125/edulearn.2021.0250   [Google Scholar]
  17. Mason C, Twomey J, Wright D, and Whitman L (2018). Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Research in Higher Education, 59(3): 382-400. https://doi.org/10.1007/s11162-017-9473-z   [Google Scholar]
  18. Namoun A and 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]
  19. Phan H, Andreotti F, Cooray N, Chén OY, and De Vos M (2018). Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering, 66(5): 1285-1296. https://doi.org/10.1109/TBME.2018.2872652   [Google Scholar] PMid:30346277 PMCid:PMC6487915
  20. Pojon M (2017). Using machine learning to predict student performance. M.Sc. Thesis, University of Tampere, Tampere, Finland.   [Google Scholar]
  21. Salal YK, Abdullaev SM, and Kumar M (2019). Educational data mining: Student performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C): 54-59.   [Google Scholar]
  22. Shetu SF, Saifuzzaman M, Moon NN, Sultana S, and Yousuf R (2021). Student’s performance prediction using data mining technique depending on overall academic status and environmental attributes. In the International Conference on Innovative Computing and Communications, Springer, Delhi, India: 757-769. https://doi.org/10.1007/978-981-15-5148-2_66   [Google Scholar]
  23. Singh M, Verma C, Kumar R, and Juneja P (2020). Towards enthusiasm prediction of Portuguese school's students towards higher education in realtime. In the International Conference on Computation, Automation and Knowledge Management, IEEE, Dubai, UAE: 421-425. https://doi.org/10.1109/ICCAKM46823.2020.9051459   [Google Scholar]
  24. Srivastava AK, Chaudhary A, Gautam A, Singh DP, and Khan R (2020). Prediction of students performance using KNN and decision tree-A machine learning approach. Strad Research, 7(9): 119-125. https://doi.org/10.37896/sr7.9/018   [Google Scholar]
  25. Syu NS, Chen YS, and Chuang YY (2018). Learning deep convolutional networks for demosaicing. ArXiv Preprint ArXiv:1802.03769. Available online at: https://arxiv.org/abs/1802.03769 
  26. Turabieh H (2019). Hybrid machine learning classifiers to predict student performance. In the 2nd International Conference on New Trends in Computing Sciences, IEEE, Amman, Jordan: 1-6. https://doi.org/10.1109/ICTCS.2019.8923093   [Google Scholar]
  27. UCI (2014). Student performance data set. UCI Machine Learning Repository. Available online at: https://archive.ics.uci.edu/ml/datasets/student+performance
  28. Ünal F (2020). Data mining for student performance prediction in education. In: Birant D (Ed.), Data mining-methods, applications and systems: 153-163. IntechOpen, London, UK. https://doi.org/10.5772/intechopen.91449   [Google Scholar]
  29. Yang J, Zhang XL, and Su P (2019). Deep-learning-based agile teaching framework of software development courses in computer science education. Procedia Computer Science, 154: 137-145. https://doi.org/10.1016/j.procs.2019.06.021   [Google Scholar]
  30. Zaffar M, Hashmani MA, Savita KS, Rizvi SSH, and Rehman M (2020). Role of FCBF feature selection in educational data mining. Mehran University Research Journal of Engineering and Technology, 39(4): 772-778. https://doi.org/10.22581/muet1982.2004.09   [Google Scholar]
  31. Zhao R, Yan R, Chen Z, Mao K, Wang P, and Gao RX (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115: 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050   [Google Scholar]