International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 15, No. 1, 8 Feb. 2023
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Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling
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Author(s)
Edith Edimo Joseph
1
Joseph Isabona
2,*
Odaro Osayande
3
Ikechi Irisi
4
Index Terms
Africa, Bayesian Optimization Algorithm, Guidance and Counseling, Hyperparameters, MLP Neural Networks, Out-of-school children
Abstract
One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.
Cite This Paper
Edith Edimo Joseph, Joseph Isabona, Odaro Osayande, Ikechi Irisi, "Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.1, pp. 1-12, 2023. DOI:10.5815/ijmecs.2023.01.01
Reference
[1]UNESCO, (2019). Meeting Commitments? Are countries on track to achieve SDG4? http://uis.unesco.org/sites/default/files/documents/ meeting-commitments-are-countries-on-track-achieve-sdg4.pdf 2
[2]UNESCO, (2022). A new school year is starting in many parts of the world. This news should bring us joy, but it also reminds us that deep inequalities persist in access to education: 244 million of children are still out of school. http://uis.unesco. org/sites
[3]C.Lockett, and L. Cornelious, L. Factors Contributing to Secondary School Dropouts in an Urban School District. Research in Higher Education Journal, vol.29, pp.1–15,2015.
[4]M. Murray, Factors Affecting Graduation and Student Dropout Rates at the University of KwaZulu-Natal. South African Journal of Science, 2014, 110 (11–12), 1–6. https://doi.org/10.1590/sajs.2014/20140008.
[5]P.A.Willging, and S. D. Johnson, Factors that Influence Students' Decision to Drop-out of Online Courses. Journal of Asynchronous Learning Network, 2009, 13(3), 115–127. https://doi.org/10.24059/olj.v8i4.1814
[6]D. J. Dockery, School Dropout Indicators, Trends, and Interventions for School Counselors Donna J. Dockery Virginia Commonwealth University, 2012.
[7]A. Moore, Factors That Cause Students to Leave Before Graduation. In Carson-Newman University. https://doi.org/10.1016/j.sbspro.2015.04.758, 2017.
[8]R. W. Rumberger, H. Addis, E. Allensworth, R. Balfanz, D. Duardo, and M. Dynarski. Preventing Drop-out in Secondary Schools. In National Center for Educational Evaluation and Regional Assistance. https://ies.ed.gov/ncee/wwc/Docs/PracticeGuide/ wwc_dropout_092617.pdf%0AAll Papers/R/Rumberger et al. 2017 - Preventing Dropout in Secondary Schools.pdf, 2017
[9]R. W. Rumberger, and S. A. Lim, Why Students Drop Out of School: A Review of 25 Years of Research. In California Dropout Research Project Report. https://www.issuelab.org/resources/11658/11658.pdf, 2008.
[10]V. X. Barrat, B. A. Berliner, and A. B Fong, When Dropping Out is Not a Permanent High School Outcome: Student Characteristics, Motivations, and Reenrollment Challenges. Journal of Education for Students Placed at Risk, 17(4), 217–233, 2012
[11]E. D. Nakpodia, An Analysis of Dropout Rate among Secondary School Student in Delta State, Nigeria (1999-2005). Journal of Social Sciences, 23(2), 99–103, https://doi.org/10.1080/09718923.2010.11892817, 2010.
[12]K. Oruko, E. Nyothach, E. Zielinski-Gutierrez, L. Mason, K. Alexander, J.Vulule, K. F. Laserson, and P. A. Phillips-Howard, He is the One Who is Providing you with Everything so Whatever he Says is What you Do: A Qualitative Study on Factors Affecting Secondary Schoolgirls' Dropout in Rural Western Kenya. PLoS ONE, 10(12), 1–14. https://doi.org/10.1371/journal.pone.0144321, 2015.
[13]M. A. Santana, E. B. Costa, B. F. S. Neto, I. C. L. Silva, and J. B. A. Rego, A Predictive Model for Identifying Students with Drop-out Profiles in Online Courses. CEUR Workshop Proceedings. https://pdfs.semanticscholar.org/.pdf, 2015.
[14]M. Neema, Data driven approach for predicting student dropout in secondary schools, PhD Thesis, The Nelson Mandela African Institution of Science and Technology, published in https://dspace.nm-aist.ac.tz/handle/20.500.12479/898
[15]E. Aguiar, N. Dame, D. Miller, B. Yuhas, and K. L. Addison, Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of not Graduating High School on Time Categories and Subject Descriptors. ACM, 93–102, 2015.
[16]C. Valiente, K. Lemery-Chalfant, J. Swanson, and M. Seiser, Prediction of Children's Academic Competence from Their Effortful Control, Relationships, and Classroom Participation, Educ Psychol. Vol.100(1), pp. 67–77, 2008. doi:10.1037/0022-0663.100.1.67.
[17]R. Halland, C. Igel, and S. Alstrup, High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 22–24, 2015.
[18]L. P. Prieto, M. J. Rodr´ıguez-Triana, M. Kusmin, and M. Laanpere, Smart School Multimodal Dataset and Challenges. CEUR Workshop Proceedings. 1828, 53–59, 2017.
[19]S. Ameri, M. J. Fard, R. B. Chinnam, and C. K Reddy,. Survival Analysis based Framework for Early Prediction of Student Dropouts. Proceedings of the ACM Conference on Information and Knowledge Management, 24–28. https://doi.org/10.1145/2983323.2983351, 2016.
[20]H. P. Singh, and H. N. Alhulail, Predicting Student-Teachers Dropout Risk and Early Identification, IEEE Access, Vol.10, 2022. DOI: 10.1109/ACCESS.2022.3141992, 2022.
[21]Hui Jiang, Laura Justice, Kelly M. Purtell, Tzu-Jung Lin, Jessica Logan, Prevalence and prediction of kindergarten-transition difficulties, Early Childhood Research Quarterly, Vol. 55, pp. 15-23, 2021, https://doi.org/10.1016/j.ecresq.2020.10.006.
[22] J. M. David and K. Balakrishnan, Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children, International Journal of Intelligent Systems and Applications, vol.12, 34-52, 2013.
[23]N Ahmad et al. Students' Performance Prediction using Artificial Neural Network, IOP Conf. Ser.: Mater. Sci. Eng. 1176 012020, 2021
[24]Carlos Felipe Rodríguez-Hernández, Mariel Musso, Eva Kyndt, Eduardo Cascallar, Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation, Computers and Education: Artificial Intelligence, Vol. 2,2021, https://doi.org/10.1016/j.caeai.2021.100018.
[25]K. Kalegele, Enabling Proactive Management of School Drop-outs Using Neural Network. Journal of Software Engineering and Applications, 13, 245-257. https://doi.org/10.4236/jsea.2020.1310016, 2020.
[26]Li, X., Zhang, Y., Cheng, H. et al. Student achievement prediction using deep neural network from multi-source campus data. Complex Intell. Syst. (2022). https://doi.org/10.1007/s40747-022-00731-8, 2022.
[27]UNESCO, UNSESCO Institute for Statistics (uis.unesco.org), 2022.
[28]V.C. Ebhota, J. Isabona, and V.M.Srivastava, Environment-Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss Prediction in Cluttered and Open Urban Microcells, Wireless Personal Communications, Vol. 104 (3), pp. 935–948, 2019.
[29]V.C. Ebhota, J. Isabona, and V.M. Srivastava, Investigation and Comparison of Generalization Ability of Multi-Layer Perceptron and Radial Basis Function Artificial Neural Networks for Signal Power Loss Prediction, International Journal on Communications Antenna and Propagation, Vol. 9 (1), pp. 43-54, 2019.
[30]V.C. Ebhota, J. Isabona, and V.M.Srivastava, Effect of Learning Rate on GRNN and MLP for the Prediction of Signal Power Loss in Microcell Sub-Urban Environment, International Journal on Communications Antenna and Propagation, Vol. 9 (1), pp. 36-45, 2019.
[31]J. Isabona, Joint Statistical and Machine Learning Approach for Practical Data‑Driven Assessment of User Throughput Quality in Microcellular Radio Networks, Wireless Personal Communication (Springer), vol, 119, pp. 1661–1680, 2021.
[32]Isabona Joseph and Ojuh.O. Divine, "Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains, I.J. Intelligent Systems and Applications, 2021, 7, 35-42
[33]J. Isabona, Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction in Open and Shadow urban Microcells, Wireless Personal Communications, Vol. 114 (3), pp.3635–3653, 2020.
[34] Odesanya Ituabhor, Joseph Isabona, Jangfa T. Zhimwang, and Ikechi Risi, Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets, International Journal of Computer Network and Information Security, 3, 63-74, 2022.
[35]K. Obahiagbon, and J. Isabona, Generalized Regression Neural Network: An Alternative Approach for Reliable Prognostic Analysis of Spatial Signal Power Loss in Cellular Broadband Networks, International Journal of Advanced Research in Physical Science, vol. 5(10): 35-42, 2018.
[36]D. O. Ojuh, and J. Isabona, (2021), Empirical and Statistical Determination of Optimal Distribution Model for Radio Frequency Mobile Networks Using Realistic Weekly Block Call Rates Indicator, I. J. Mathematical Sciences and Computing, 2021, 3, 12-23.
[37]D.O. Ojuh, and J. Isabona (2021) Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band, J. Intelligent Systems and Applications, 2021, 4, 14-23
[38]J. Isabona, and S. Azi, Measurement, Modeling and Analysis of Received Signal Strength at 800MHz and 1900MHz in Antenna Beam Tilt Cellular Mobile Environment, Elixir Comp. Sci. & Engg. 54 (2013) 12300-12303
[39]J. Isabona, (2019), Maximum likelihood Parameter based Estimation for In-depth Prognosis Investigation of Stochastic Electric Field Strength Data, BIU Journal of Basic and Applied Sciences, vol. 4(1): 127 – 136, 2019.
[40]Ekpenyong, M., Umoren. E., & Isabona, J. (009). A Rain Attenuation Model for Predicting Fading Effect on Wireless Communication Systems in the Tropics,” Niger. J. Sp. Res., 6, 21–32.