PREDICTIVE ANALYTICS OF ACADEMIC PERFORMANCE OF SENIOR HIGH SCHOOL (SHS) STUDENTS: A CASE STUDY OF SUNYANI SHS

Authors

  • Adjei-Pokuaa Henrietta Department of Computer Science and Informatics, University of Energy and Natural Resources, Ghana https://orcid.org/0000-0002-6644-1453
  • Prof Adebayo F. Adekoya Department of Computer Science and Informatics, University of Energy and Natural Resources, Ghana https://orcid.org/0000-0002-5029-2393

DOI:

https://doi.org/10.29121/ijetmr.v9.i2.2022.1088

Keywords:

Learning Management, Demographic Data, Effectively Predict, Random Forest Algorithm

Abstract

Due to the availability and increasing adoption of technology in learning management systems, online admission systems, school management systems, and educational databases have expanded in recent years.

Motivation/Background: Literature shows that these data contain vital and relevant information that could be used to monitor and advise students’ so that their performance could be enhanced. In this study, the random forest algorithm is proposed to identify and examine the factors that influence students’ performance in WASSCE. Also, predict the future performance of students in WASSCE.

Method: A total of one thousand five hundred and twenty students’ data were selected from Sunyani SHS. The results revealed that demographic data (age and gender) do not influence the performance of students’ in their final WASSCE.

Results: However, an accuracy of 89.4% with error metrics (RMSE) 0.001639 and MAPE error of 0.001321 revealed that the proposed model could effectively predict the performance of students in the WASSCE.

Downloads

Download data is not yet available.

References

Adejo, O. W., & Connolly, T. (2018). Predicting Student Academic Performance Using Multi-Model Heterogeneous Ensemble Approach. Journal Of Applied Research In Higher Education, 10(1), 61-75. Retrieved from https://doi.org/10.1108/JARHE-09-2017-0113 DOI: https://doi.org/10.1108/JARHE-09-2017-0113

Agrawal, H., & Mavani, H. (2015). Student Performance Prediction Using Machine Learning. 4(03), 111-113. Retrieved from https://doi.org/10.17577/IJERTV4IS030127 DOI: https://doi.org/10.17577/IJERTV4IS030127

Ahmed, A. B. E. D., & Ibrahim, S. E. (2014). Data Mining : A Prediction For Student's Performance Using Classification Method. World Journal Of Computer Application And Technology, 2(2), 43-47. Retrieved from https://doi.org/10.13189/wjcat.2014.020203 DOI: https://doi.org/10.13189/wjcat.2014.020203

Analyticsvidhya.Com. (2010). Random Forest Algorithm. Retrieved from Https://Www.Google.Com/Imgres?Imgurl=Https%3A%2F%2Fwww.Analyticsvidhya.Com%2Fwp-Content%2Fuploads%2F2015%2F06%2Frandom-Forest7.Png&Imgrefurl=Https%3A%2F%2Fwww.Analyticsvidhya.Com%2Fblog%2F2015%2F06%2Ftuning-Random-Forest-Model%2F&Tbnid=Bldygobmf_Oqom&Vet=12ahukewibtpbop-_Qahvtlkqkhbfndnuqmygieguiarc3aq.I&Docid=Gp-

Attuquayefio, Niiboi, S., & Addo, H. (2014). Using The UTAUT Model To Analyze Students' ICT Adoption. International Journal Of Education & Development Using Information & Communication Technology, 10(3), 75-86. Retrieved from Http://Ezproxy.Usq.Edu.Au/Login?Url=Http://Search.Ebscohost.Com/Login.Aspx?Direct=True&Db=Ehh&AN=97923459&Site=Ehost-Live

Berhanu, F. (2015). Students ' Performance Prediction Based On Their Academic Record. International Journal Of Computer Applications, 131(5), 27-35. Retrieved from https://doi.org/10.5120/ijca2015907348 DOI: https://doi.org/10.5120/ijca2015907348

Bhardwaj, B. K., & Pal, S. (2011). Data Mining : A Prediction For Performance Improvement Using Classification. International Journal Of Computer Science And Information Security, 9(4), 136-140.

Bosson-Amedenu, S. (2017). Predictive Validity Of Mathematics Mock Examination Results Of Senior And Junior High School Students' Performance In WASSCE And BECE In Ghana. Asian Research Journal Of Mathematics, 3(4), 1-8. Retrieved from https://doi.org/10.9734/ARJOM/2017/32328 DOI: https://doi.org/10.9734/ARJOM/2017/32328

Chen, J.-F., Hsieh, H.-N., & Quang, H. Do. (2014). Predicting Student Academic Performance : A Comparison Of Two Meta-Heuristic Algorithms Inspired By Cuckoo Birds For Training Neural Networks. Algorithms, 7(4), 538-553. Retrieved from https://doi.org/10.3390/a7040538 DOI: https://doi.org/10.3390/a7040538

Cortez, P., & Silva, A. (2008). Using Data Mining To Predict Secondary School Student Performance. 5th Annual Future Business Technology Conference, 2003(2000), 5-12. Retrieved from Https://Doi.Org/10.13140/RG.2.1.1465.8328

Devasia, T., Vinushree, T. P., & Hegde, V. (2016). Prediction Of Students Performance Using Educational Data Mining. En 16. Retrieved from https://doi.org/10.1109/SAPIENCE.2016.7684167 DOI: https://doi.org/10.1109/SAPIENCE.2016.7684167

Egbenya, G. R. K., & Halm, E. (2016). A Comparative Study Of Students ' Performance For The Three Year And The Four Year Programmes In Mfantsipim And University Of Cape Coast, Practice Senior High Schools In Cape Coast , Ghana. Internal Journal Of Innovative Reserach & Development, 5(3), 114-129.

Fleischer, J. E. (2015). Information Communication Technology Usage Patterns In Second Cycle Schools : A Study Of Two Selected Senior High Schools In Ghana (Issue 10232161).

Goga, M., Kuyoro, S., & Goga, N. (2015). A Recommender For Improving The Student Academic Performance. Procedia - Social And Behavioral Sciences, 180(November 2014), 1481-1488. Retrieved from https://doi.org/10.1016/j.sbspro.2015.02.296 DOI: https://doi.org/10.1016/j.sbspro.2015.02.296

Guo, B., Zhang, R., Xu, G., Shi, C., & Yang, L. (2015). Predicting Students Performance In Educational Data Mining. 2015 International Symposium On Educational Technology (ISET), 125-128. Retrieved from https://doi.org/10.1109/ISET.2015.33 DOI: https://doi.org/10.1109/ISET.2015.33

Hedén, W. (2016). Predicting Hourly Residential Energy Consumption Using Random Forest And Support Vector Regression An Analysis Of The Impact Of Household Clustering On The Performance Accuracy. KTH Royal Institute Of Technology.

Khasanah, A. U., & Harwati. (2017). A Comparative Study To Predict Student's Performance Using Educational Data Mining Techniques. IOP Conference Series: Materials Science And Engineering, 215(1). Retrieved from https://doi.org/10.1088/1757-899X/215/1/012036 DOI: https://doi.org/10.1088/1757-899X/215/1/012036

Kieti, J. M. (2017). An Investigation Into Factors Influencing Students' Academic Performance In Public Secondary Schools In Matungulu Sub-County, Machakos County. In South Eastern Kenya University. Retrieved from https://doi.org/10.1111/j.1469-7610.2010.02280.x DOI: https://doi.org/10.1111/j.1469-7610.2010.02280.x

Kumar, M., & Thenmozhi, M. (2006). Forecasting Stock Index Movement: A Comparison Of Support Vector Machines And Random Forest. Indian Institute Of Capital Markets 9th Capital Markets Conference Paper, 1-16. Retrieved from https://doi.org/10.2139/ssrn.876544 DOI: https://doi.org/10.2139/ssrn.876544

Leo, B., & Adele, C. (2002). Random Forests. Retrieved from Https://Www.Stat.Berkeley.Edu/~Breiman/Randomforests/Cc_Home.Htm

Li, K. F., Rusk, D., & Song, F. (2013). Predicting Student Academic Performance. 2013 Seventh International Conference On Complex, Intelligent, And Software Intensive Systems, 27-33. Retrieved from https://doi.org/10.1109/CISIS.2013.15 DOI: https://doi.org/10.1109/CISIS.2013.15

Livieris, I. E., Tassos, A. M., & Panagiotis, P. (2016). A Decision Support System For Predicting Student Performance. International Journal Of Innovative Research In Computer And Communication Engineering, 02(12), 7232-7237. Retrieved from https://doi.org/10.15680/IJIRCCE.2014.0212015 DOI: https://doi.org/10.15680/IJIRCCE.2014.0212015

Mahbina, A. M., & Zamil, K. M. S. (2018). GIS-Based Analysis Of Changing Surface Water In Rajshahi City Corporation Area Using Support Vector Machine (SVM), Decision Tree & Random Forest Technique. Machine Learning Research, 3(2), 11-17. Retrieved from https://doi.org/10.11648/j.mlr.20180302.11 DOI: https://doi.org/10.11648/j.mlr.20180302.11

Mohamed, A., Rizaner, A., & Hakan, A. (2016). Using Data Mining To Predict Instructor Performance. Procedia - Procedia Computer Science, 102(August), 137-142. Retrieved from https://doi.org/10.1016/j.procs.2016.09.380 DOI: https://doi.org/10.1016/j.procs.2016.09.380

Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting General Academic Performance And Identifying The Differential Contribution Of Participating Variables Using Artificial Neural Networks. Frontline Learning Research, 1(1), 42-71. Retrieved from https://doi.org/10.14786/flr.v1i1.13 DOI: https://doi.org/10.14786/flr.v1i1.13

Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A Comprehensive Evaluation Of Ensemble Learning For Stock-Market Prediction. Journal Of Big Data, 7(1). Retrieved from https://doi.org/10.1186/s40537-020-00299-5 DOI: https://doi.org/10.1186/s40537-020-00299-5

Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting Students' Academic Performance Using Artificial Neural Network : A Case Study Of An Engineering Course. The Pacific Journal Of Science And Technology, 9(1), 72-79.

Osmanbegovic, E., & Suljic, M. (2012). Data Mining Approach For Predicting Student Performance. Journal Of Economics And Business, X (1), 3-12.

Osmanbegović, E., Suljić, M., & Agić, H. (2014). Determining Dominant Factor For Students Performance Prediction By Using Data Mining. Vitez-Tuzla-Zagreb-Beograd-Bucharest, XVII (34), 147-158.

Pandey, M., & Taruna, S. (2016). Towards The Integration Of Multiple Classifier Pertaining To The Student ' S Performance Prediction. Perspectives In Science, 8, 364-366. Retrieved from https://doi.org/10.1016/j.pisc.2016.04.076 DOI: https://doi.org/10.1016/j.pisc.2016.04.076

Purwaningsih, N., Arief, D. R., Purwaningsih, N., & Arief, D. R. (2018). Predicting Students ' Performance In English Class. AIP, 020020. Retrieved from https://doi.org/10.1063/1.5042876 DOI: https://doi.org/10.1063/1.5042876

Rajesh, S. B. (2018). Introduction To Decision Trees. Https://Medium.Com/Greyatom/Decision-Trees-A-Simple-Way-To-Visualize-A-Decision-Dc506a403aeb#:~:Text=A Decision Tree Is A Flowchart-Like Structure In Which,Taken After Computing All Attributes).

Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine Learning Predictive Models For Mineral Prospectivity: An Evaluation Of Neural Networks, Random Forest, Regression Trees And Support Vector Machines. Ore Geology Reviews, 71, 804-818. Retrieved from https://doi.org/10.1016/j.oregeorev.2015.01.001 DOI: https://doi.org/10.1016/j.oregeorev.2015.01.001

Tan, Z., Yan, Z., & Zhu, G. (2019). Stock Selection With Random Forest : An Exploitation Of Excess Return In The Chinese Stock Market. Heliyon, 5(8), E02310. Retrieved from https://doi.org/10.1016/j.heliyon.2019.e02310 DOI: https://doi.org/10.1016/j.heliyon.2019.e02310

Tran, T., Dang, H., Dinh, V., Truong, T.-M.-N., Vuong, T., & Phan, X. (2017). Performance Prediction For Students : A Multi-Strategy Approach. CYBERNETICS AND INFORMATION TECHNOLOGIES, 17(2), 164-182. Retrieved from https://doi.org/10.1515/cait-2017-0024 DOI: https://doi.org/10.1515/cait-2017-0024

Tuen, W., Leung, V., Yee, T., Pan, W., Wu, C., Lung, S. C., & Spengler, J. D. (2019). Landscape And Urban Planning How Is Environmental Greenness Related To Students ' Academic Performance In English And Mathematics ? Landscape And Urban Planning, 181(1), 118-124. Retrieved from https://doi.org/10.1016/j.landurbplan.2018.09.021 DOI: https://doi.org/10.1016/j.landurbplan.2018.09.021

Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A Comparative Study On Machine Learning Algorithms For Smart Manufacturing: Tool Wear Prediction Using Random Forests. Journal Of Manufacturing Science And Engineering, 139(7), 071018. Retrieved from https://doi.org/10.1115/1.4036350 DOI: https://doi.org/10.1115/1.4036350

Yadav, S. K., & Pal, S. (2012). Data Mining : A Prediction For Performance Improvement Of Engineering Students Using Classification. World Of Computer Science And Information Technology Journal WCSIT, 2(2), 51-56. Retrieved from https://doi.org/10.1142/9789812771728_0012 DOI: https://doi.org/10.1142/9789812771728_0012

Yeboah, Y. K. (2014). Investigating The Low Performance Of Students' English In The Basic Education Certificate Examination In The Sunyani Municipality (Issue 10357198) [UNIVERSITY OF GHANA, LEGON]. Retrieved from https://doi.org/10.1038/253004b0 DOI: https://doi.org/10.1038/253004b0

Yusif, H. M., Yussof, I., & Noor, A. H. S. M. (2011). Determinants Of Students Academic Perform- Ance In Senior High Schools : A Binary Logit Approach. 31(3), 107-117. Retrieved from https://doi.org/10.4314/just.v31i3.12 DOI: https://doi.org/10.4314/just.v31i3.12

Downloads

Published

2022-02-21

How to Cite

Adjei-Pokuaa, H., & F. Adekoya, A. . (2022). PREDICTIVE ANALYTICS OF ACADEMIC PERFORMANCE OF SENIOR HIGH SCHOOL (SHS) STUDENTS: A CASE STUDY OF SUNYANI SHS. International Journal of Engineering Technologies and Management Research, 9(2), 64–81. https://doi.org/10.29121/ijetmr.v9.i2.2022.1088