HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education

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Author(s)

Marina. B 1,* A. Senthilrajan 2

1. Alagappa University, Asst. Prof, St. Anne’s Degree College for Women, Ulsoor, Bangalore

2. Dept of Computational Logistics, Alagappa University, Karaikudi, Tamil Nadu

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2023.02.02

Received: 11 Jul. 2022 / Revised: 20 Aug. 2022 / Accepted: 28 Sep. 2022 / Published: 8 Apr. 2023

Index Terms

Education, Dropout, Physically Impaired, Feature selection, Accuracy

Abstract

Education plays a significant role in individuals’ development and economic growth of the developing coun-tries like India. Dropout of students from their studies is the major concern for any order of education. Some models for predicting the dropout of students are developed with several factors. Many of them lacked consistencies as they backed their studies with the academic performance of the students. Especially, for those students suffered with physical impairment the drop out depends on several external factors. Students drop out of school for a variety of reasons, including financial difficulties, parents' unwillingness, distance and a lack of basic amenities, poor educational quality, an inadequate school environment and building, overcrowded classrooms, improper languages of instruction, carelessness on the part of teachers, and security issues in girls' schools. Hence, this work proposes a novel HFIPO-DPNN to predicting the physically handicapped student’s dropout from School also to predict the student dropout rooted on the previous semester marks. The proposed model enclosed the hybrid firefly and improved particle swarm algorithm to optimize the feature selection that influence the dropout of hearing-impaired students. The optimized feature data are used to predict the dropout with the novel DPNN. The optimized data was split and used for training the DPNN. The testing data is used to evaluate the performance of the proposed framework. The outcome for the proposed framework is evaluated on several metrics. The accuracy of the proposed model is about 99.02%. The HFIPO-DPNN framework can be enhanced for predicting the dropout for students with other disabilities. The optimization revealed that factors other than family factors should be taken into account when predicting dropout.

Cite This Paper

Marina. B, A. Senthilrajan, "HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.2, pp. 12-25, 2023. DOI:10.5815/ijmecs.2023.02.02

Reference

[1]Latif, A., Choudhary, A. I., & Hammayun, A. A.: “Economic effects of student dropouts: A comparative study”.   Journal of global economics. 3(2), 137. (2015).

[2]Stillwell, R.: “Public School Graduates and Dropouts from the Common Core of Data: School Year 2007-08. First Look”. NCES 2010-341. National Center for Education Statistics, (2010).

[3]Swanson, C. B., & Schneider, B.: “Students on the move: Residential and educational mobility in America's school”. Sociology of Education.54-67. (1999).

[4]Ahmad, Wasim.:”Higher Education for Persons with Disabilities in India: Challenges and Concerns”. Journal of Disability Management and Rehabilitation 2, no. 1 ,pp : 1-4, (2017)

[5]Census 2011

[6]Xu, J., Moon, K. H., & Van Der Schaar, M.: “A machine learning approach for tracking and predicting student performance in degree programs”. IEEE Journal of Selected Topics in Signal Processing, 11(5), 742-753. (2017).

[7]Sweeney, M., Rangwala, H., Lester, J., & Johri, A.: “Next-term student performance prediction: A recommender systems ap-proach”. arXiv preprint arXiv:1604.01840. (2016).

[8]Santos, O. C.& Boticario, J. G.:  “Practical guidelines for designing and evaluating educationally oriented recommendations”, Computers & Education, vol. 81, pp. 354-374. (2015).

[9]Ahmed, A.B.E.D. and Elaraby, I.S.: “Data Mining:A prediction for Student's Performance Using Classification Method”. World Journal of Computer Application and Technology, 2(2), pp.43-47.(2014).

[10]Amjad Abu Saa.: “Educational Data Mining & Student’s Performance Prediction”. International Journal of Advanced Computer Science and Applications. Vol. 7, No. 5, (2016).

[11]Yukselturk E, Ozekes S, Türel YK.: “Predicting dropout student: an application of data mining methods in an online education program”. European Journal of Open, Distance and E-learning. Jul 1;17(1):118–33 (2014).

[12]Makhtar, M., Nawang, H., and Shamsuddin, S. N. W.: “Analysis onstudents performance using Naïve Bayes classifier” J. Theor. Appl.Inf. Technol. 95(16):3993–3999, (2017).

[13]Okubo, F., Yamashita, T., Shimada, A., and Ogata, H.: “A neuralnetwork approach for students' performance prediction”. In:Proceedings of the Seventh International Learning Analytics &Knowledge Conference, pp. 598-599, (2017).

[14]Bita Akram, Bradford Mott, Wookhee Min, Kristy Elizabeth Boyer, Eric Wiebe,and James Lester.: “Improving Stealth As-sessment in Game-based Learningwith LSTM-based Analytics”. In Proceedings of the 11th International EducationalData Mining Conference. Pp. 208–218, (2018).

[15]Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami,Leonidas J. Guibas, and Jascha Sohl-Dickstein.: “Deep knowledge tracing”. neural information processing systems, 505–513 (2015).

[16]Thai-Nghe N., Drumond L., Krohn-Grimberghe A and Schmidt-Thieme L.: “Recommender system for predicting student per-formance”. In Procedia Computer Science, Volume 1,Issue 2, pp 2811-2819(2010).

[17]Portugal I, Alencar P, Cowan D.: “The use of Machine Learning Algorithms in Recommender Systems”. A Systematic Review. arXiv:1511.05263v4 [cs.SE].

[18]Aher, S. B. and L.M.R.J., L.: “Best Combination of Machine Learning Algorithms for Course Recommendation System in”, International Journal of Computer Applications (0975, 41(6), pp. 2–11. (2012). doi: 10.5120/5542-7598.

[19]Abu-Naser, S. S., Zaqout, I. S., Abu Ghosh, M., Atallah, R. R., & Alajrami, E.: “Predicting student performance using artificial neural network”: Faculty of Engineering and Information Technology. (2015).

[20]Zacharis, N. Z.: “Predicting student academic performance in blended learning using Artificial Neural Networks”. International Journal of Artificial Intelligence and Applications, 7(5), 17-29 (2016).

[21]Castro-Wunsch, K., Ahadi, A., & Petersen, A.: “Evaluating neural networks as a method for identifying students in need of assistance”. ACM SIGCSE technical symposium on computer science education (pp. 111-116) (2017, March).

[22]Liu, A., 2016, June. “A Study on the Current Situation of Innovation and Entrepre   neurship of Chinese College Students”. In 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017) (pp. 9-12). Atlantis Press.

[23]Alom, B.M. and Courtney, M., 2018. “Educational data mining: a case study perspective from primary to university education in Australia”. International Journal of Information Technology and Computer Science, 10(2), pp.1-9.

[24]Lv, X., Wang, Y., Deng, J., Zhang, G., & Zhang, L:. “Improved Particle Swarm Optimization Algorithm Rooted on Last-Eliminated Principle and Enhanced Information Sharing”. Computational intelligence and neuroscience, (2018).

[25]Xu, H., Yu, S., Chen, J., & Zuo, X.: “An Improved Firefly Algorithm for Feature Selection in Classification”. Wireless Personal Communications. doi:10.1007/s11277-018-5309-1 (2018).

[26]Patro, S., & Sahu, K. K.:  “Normalization: A preprocessing stage”. 2015 arXiv preprint arXiv:1503.06462.

[27]Sivakumar, S., Venkataraman, S. and Selvaraj, R., 2016. “Predictive modeling of student dropout indicators in educational data mining using improved decision tree”. Indian Journal of Science and Technology, 9(4), pp.1-5.

[28]Kostopoulos, G., Kotsiantis, S. and Pintelas, P., 2015, October. “Estimating student dropout in distance higher education using semi-supervised techniques”. In Proceedings of the 19th Panhellenic Conference on Informatics (pp. 38-43).

[29]Meedech, P., Iam-On, N., & Boongoen, T. “Prediction of Student Dropout Using Personal Profile and Data Mining Approach”. Intelligent and Evolutionary Systems, 143–155. 2015. doi:10.1007/978-3-319-27000-5_12 

[30]Sarker, M.N.I., Wu, M. and Hossin, M.A., 2019. “Economic effect of school dropout in Bangladesh”. International journal of information and education technology, 9(2), pp.136-142.

[31]Xenos, M., Pierrakeas, C. and Pintelas, P., 2002. “A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University”. Computers & Education, 39(4), pp.361-377.

[32]Pierrakeas, C., Xenos, M., Panagiotakopoulos, C. and Vergidis, D., 2004. “A comparative study of dropout rates and causes for two different distance education courses”. International Review of Research in Open and Distributed Learning, 5(2), pp.1-15.

[33]Alika, I.H. and Egbochuko, E.O., 2009. “Drop out from school among girls in edo state: implications for counselling”. Edo Journal of Counselling, 2(2), pp.135-141.