Skip to main content

Advertisement

SpringerLink
Log in

Modeling education impact: a machine learning-based approach for improving the quality of school education

  • Published:
Journal of Computers in Education Aims and scope Submit manuscript

Abstract

Improving literacy through efficacious school education is an essential objective of a society to reduce poverty and inequality. Thus, a micro-level analysis conducted using Geospatial Artificial Intelligence Modeling (GeoAI) and Machine learning (ML) identified the critical factors affecting student success rates in the West Garo Hills (WGH) district of Meghalaya. A total of 69 data variables comprising school infrastructure, teachers, student enrollment and performance, and relevant teaching–learning information assimilated with regional demographic and assets data surrounding the schools are considered. The methodology involves data aggregation, statistical analysis including dimensionality reduction, feature engineering, and subsequent geospatial AI fusion, spatial autocorrelation, and geospatial buffer analysis for deducing valuable insights. Random Forest model shows that the presence of boundary walls, number of qualified and total teachers, school category, Pupil–Teacher ratio, enrollments, a company of a playground, no. of classrooms, electricity, and drinking water (reflects better infrastructure), and literate/Illiterate ratio are some of the most important factors affecting student performance. A boundary wall in schools can be an important variable as it helps to retain students within school premises resulting in better pedagogical impact. Significant gaps were observed in the presence of schools at all four levels, i.e., primary, upper primary, secondary, and senior secondary, in 7 blocks of WGH. The decision tree regressor model is used for forecasting the pass percentage of students in subsequent years with an accuracy of 93%. The research creates a novel microanalysis school education tool for the stakeholders, and reforms based on these findings can lead to a solemn positive impact in the education sector.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The datasets collected, generated, or analyzed during the present investigations are available from the corresponding author upon reasonable request.

Abbreviations

ML:

Machine learning

A.I.:

Artificial intelligence

GeoAI:

Geospatial artificial intelligence

WGH:

West Garo Hills

RMSE:

Root mean square deviation

PTR:

Pupil–Teacher ratio

CAR:

Classroom adequacy ratio

PGIs:

Performance grading indicators

References

  • Abraha, A. T. (2019). Analyzing spatial and non-spatial factors that influence educational quality of primary schools in emerging regions of Ethiopia: Evidence from geospatial analysis and administrative time series data. Journal of Geography and Regional Planning, 12(1), 10–19.

    Article  Google Scholar 

  • Agita, D., Karina, S. & Kristaps, S. (2020). Class attendance as a factor affecting academic performance. In Proceedings of the International Scientific Conference, Rezekne, 20th May 2020; Vol. 6, pp. 578–594.

  • Ajit, K., Pallavi, K., & Mohammad, M. (2020). Examining the linkage between class attendance at university and academic performance in an International Branch Campus setting. Research in Comparative and International Education, 15(4), 371–390.

    Article  Google Scholar 

  • Ali, S.H., Wid, A. A., & Alaa, K.H. (2020). Student Performance Prediction Model Based on Supervised Machine Learning Algorithms. In IOP Conference Series: Materials Science and Engineering, 2nd International Scientific Conference of Al-Ayen University (ISCAU-2020) 15–16th July 2020, Thi-Qar, Iraq, pp. 928. Doi:https://doi.org/10.1088/1757-899X/928/3/032019.

  • Amal, A.-A., Zuhoor, A.-K., Yassine, J., & Naoufel, K. (2019). Mining the students’ chat conversations in a personalized e-learning environment. International Journal of Emerging Technologies in Learning (IJET), 14(23), 98–124.

    Article  Google Scholar 

  • Anjali, A. (2017). Sanitation and education: American Economic Journal. Applied Economics, 9(2), 23–59. https://doi.org/10.1257/app.20150083

    Article  Google Scholar 

  • Arun, C.M. (2021). Educational Development in the North Eastern Region of India. Fellow NIEPA, New Delhi (INDIA): 1–4. Retrieved September 12, 2022, from https://educationforallinindia.com/educational-development-in-the-north-east/.

  • Balqis, A.B., Nazar, Z., & Elfadil, A.M. (2019). Using educational data mining techniques to predict student performance. In Proceedings of the 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, 19–21st November, 2019, 1–5.

  • Bhanu, P. P. (2019). Education in north-eastern India: A study of supply and demand in secondary teacher education. An International Journal of Education and Applied Social Science, 10(1), 45–51.

    Google Scholar 

  • Bijaya, N., & Ramkrishna, M. (2015). Effect of school’s physical facilities on learning and outcomes of students in Nepal. Journal for Studies in Management and Planning, 1(6), 266–279.

    Google Scholar 

  • Brenton, B., Stephen, T., & Ian, J. (2013). Relations between the school physical environment and school social capital with student physical activity levels. BMC Public Health, 13(1), 1191–1199.

    Article  Google Scholar 

  • Dharmendera, K. M., Rajeev, T., & Sonam, A. (2023). An evaluation of primary schools and its accessibility using GIS techniques: A case study of Prayagraj district, India. GeoJournal, 88, 1921–1951.

    Google Scholar 

  • Dhi, B. (2015). Comparison and analysis of spatial elementary school distribution and teacher certification by digital mapping based on geographic information system. Dinamika, 7(2), 53–70.

    Google Scholar 

  • District Census. (2011). "District Census 2011". Census2011.Co.in. 2011. Retrieved September 30, 2011, from https://www.census2011.co.in/.

  • Fabiyi, O. O., & Ogunyemi, S. A. (2015). Spatial distribution and accessibility to post primary educational institution in Ogun State, Southwestern Nigeria: Case study of Yewa South Local Government Area, Nigeria. Journal of Scientific Research and Reports, 5(7), 542–552.

    Article  Google Scholar 

  • Federal Ministry of education (FMoE). (2017). Education Statistics Annual Abstract, 2016/17 Available at: http://www.moe.gov.et/documents/20182/0/Statistics+2009+final+1/ca93f33d-0540-468e-9806-0e6032f8d848?version=1.0.

  • Filip, K.D., Mario, B., & Nikica, H. (2018). Explainable artificial intelligence: A survey. paper presented at the 2018 41st international convention on information and communication technology. Electronics and microelectronics (MIPRO), Publisher: IEEE. Doi: https://doi.org/10.23919/MIPRO.2018.8400040

  • De la Helen, F., Carolina, R., Salado, M. J., Carrasco, J., & Neutens, T. (2013). Socio-spatial inequality in education facilities in the Concepción metropolitan area (Chile). Current Urban Studies, 1(4), 117–129.

    Article  Google Scholar 

  • Hyoungjong, K., & Dong-Eun, R. (2019). Toilets for education: Evidence from Kenya’s primary school-level data. International Journal of Educational Development, 70, 102090. https://doi.org/10.1016/j.ijedudev.2019.102090

    Article  Google Scholar 

  • Ian, H.W., Eibe, F., & Mark, A.H. (2011). Data mining: practical machine learning tools and techniques: A volume in the morgan kaufmann series in data management systems. 3rd edn, 2011. https://www.sciencedirect.com/book/9780123748560/data-mining-practical-machine-learning-tools-and-techniques.

  • Ijaz, K., Abir, A.S., Abdul, R.A., & Nafaa, J. (2019). Tracking Student Performance in Introductory Programming by Means of Machine Learning. In paper Presented at the 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–15.

  • Ijaz, K., Abdul, R. A., Nafaa, J., & Mohammed, N. M. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, 8(1), 17–35. https://doi.org/10.1186/s40561-021-00161-y

    Article  Google Scholar 

  • Ioannis, E. L., Konstantina, D., Vassilis, T. T., Tassos, A. M., & Panagiotis, P. (2018). Predicting secondary school students’ performance utilizing a semi-supervised learning approach. Journal of Educational Computing Research, 57(2), 448–470.

    Google Scholar 

  • Jan, D. V. (2005). Relations between student learning patterns and personal and contextual factors and academic performance. Higher Education, 49, 205–234. https://doi.org/10.1007/s10734-004-6664-2

    Article  Google Scholar 

  • John, H. (2009). Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement. https://www.routledge.com/Visible-Learning-A-Synthesis-of-Over-800-Meta-Analyses-Relating-to-Achievement/Hattie/p/book/9780415476188#

  • John, O. K., Misia, M. M. K., & Mobegi, F. O. (2017). School climate as a determinant of pupils’ academic performance in public primary schools in Marani Sub-County, Kenya. International Journal of Education and Research, 5(5), 265–278.

    Google Scholar 

  • Kathryn, G., & Margaret, S. (2005). The impact of school resources on student performance: Study of Private Schools in the United Kingdom. Industrial and Labour Relations Review, 58(3), 435–451.

    Article  Google Scholar 

  • Khalid, A. R., & Hamdy, I.E.-G. (2013). GIS as an efficient tool to manage educational services and infrastructure in Kuwait. Journal of Geographic Information System, 5(1), 1–12.

    Google Scholar 

  • Kissinger, S., Patrick, O., Sadiq, H., Solomon, S. O., Balogun, O. S., & Friday, J. A. (2020). Analyzing student performance in programming education using classification techniques. International Journal of Emerging Technologies in Learning, 15(2), 127–144.

    Article  Google Scholar 

  • Leena, H. Alamri, Ranim, S.A., Mona, S.A., Dana, K.A., Irfan, U.K., & Nida, A. (2020). Predicting student academic performance using support vector machine and random forest, ICETM 2020, December 17–19, 2020, London

  • Mahmoud, A. N., Riyadh, M., Elfadil, A. M., & Mirna, N. (2023). Analysis of the factors affecting student performance using a neuro-fuzzy approach. Education Sciences, 13(3), 313–327.

    Article  Google Scholar 

  • Marc, C., Peter, L., Yohana, M., & Andrew, I. (2014). Using participatory mapping to assess service catchment and coverage: guidance from the Iringa participatory mapping exercise. Available at: https://www.measureevaluation.org/resources/publications/sr-14-109.

  • Maryam, G., & Goran, C. (2019). Does big data enhance firm innovation competency? The mediating role of data-driven insights. Journal of Business Research, 104, 69–84.

    Article  Google Scholar 

  • Maureen, K. M., Yang, Y., & Fanuel, K. (2014). Analysis of factors causing poor passing rates and high dropout rates among primary school girls in Malawi. World Journal of Education, 4(1), 48–61.

    Google Scholar 

  • Ministry of Works and Urban Development (MWUD). (2017). Urban planning implementation manual (Mathos Asfaw) Addis Ababa, Ethiopia, p. 120. Available at: https://allafrica.com/stories/200705150782.html.

  • Mitchell, R., Michalski, J., & Carbonell, T. (2013). Machine learning an artificial intelligence approach. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Why should machines learn? Springer.

    Google Scholar 

  • Mohammed, N. Y., & Mohammed, A. (2022). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes, 51(2), 916–934.

    Article  Google Scholar 

  • Moses, M. N., Douglas, M., & Francis, O. M. (2012). Planning and analysis of educational facilities using GIS: A case study of Busia County, Kenya. Applied Geoinformatics for Society and Environment, 5, 261–269.

    Google Scholar 

  • Mrityunjoy, J., Ansar, K., & Soumendu, C. (2014). Issues in quality of primary education in backward areas of Jhargram sub-division: An exploration of policy options for adjustment, revitalization and expansion. IOSR, 4(6), 58–67.

    Google Scholar 

  • Musarat, A., Sundus, N., Faqiha, N., Fozia, P., & Sameen, A. (2014). Impact of parental education and socioeconomic status on academic achievements of university students. European Journal of Psychology and Educational Research, 1(1), 1–9.

    Google Scholar 

  • Nadine, C.-R., Pedro, R., Tiago, O., & Aleš, P. (2019). Unlocking the drivers of big data analytics value in firms. Journal Business Research, 97, 160–173.

    Article  Google Scholar 

  • NCTE: National Council for Teacher Education. (2009). National Curriculum Framework for Teacher Education. Retrieved September 9, 2022, from https://ncte.gov.in/website/PDF/NCFTE_2009.pdf.

  • Oloko-oba, M., Ogunyemi, A., Rahmon, B., & John, O. (2016). Spatial distribution of primary schools in ilorin west local government area, Kwara State, Nigeria. Journal of Scientific Research and Reports, 9(6), 1–10.

    Article  Google Scholar 

  • Oloruntoba, S. A., & Akinode, J. L. (2017). Student academic performance prediction using support vector machine. International Journal of Engineering Sciences & Research Technology, 6(12), 588–598.

    Google Scholar 

  • Oluwadare, O. I., & Olujimi, J. (2011). Regional analysis of locations of public educational facilities in Nigeria: The Akure region experience. Journal of Geography and Regional Planning, 4(7), 428–442.

    Google Scholar 

  • Pengfei, T., Jingjing, X., Jing, L., & Guolei, C. (2014). Spatial accessibility analysis of primary schools at the county level based on the improved potential model: A case study of Xiantao City. Hubei Province. Progress in Geography, 36(6), 697–708.

    Google Scholar 

  • Indian Standard IS 8827-1978. (2006). Recommendations for basic requirements of school buildings. https://law.resource.org/pub/in/bis/S03/is.8827.1978.pdf.

  • Ronak, E., Yongcheng, Z., Qizhi, Z., Yilun, Hu., Bohan, C., Mohammed, A. S., Shirong, Z., & Jose, G. S. P. (2020). Role of absence in academic success: An analysis using visualization tools. Smart Learning Environments, 7(2), 1–25.

    Google Scholar 

  • Rosa, L. U. C. (2022). Accuracy comparison between statistical and computational classifiers applied for predicting student performance in online higher education. Education and Information Technologies, 27, 11565–11590.

    Article  Google Scholar 

  • Sam, J. (2016). How does classroom composition affect learning outcomes in Ugandan primary schools? International Journal of Educational Development, 48, 66–78.

    Article  Google Scholar 

  • Samlesh, C., Kaylash, C., Avinesh, P., & Vishal, C. (2021). Perceived causes of students poor performance in mathematics: A case study at Ba and Tavua Secondary Schools. Frontiers in Applied Mathematics and Statistics, 7, 614408.

    Article  Google Scholar 

  • Sateesh, G. M., & Sekher, T. V. (2014). Factors leading to school dropouts in India: An analysis of national family health survey data. IOSR Journal of Research & Method in Education (IOSR-JRME), 4(6), 75–83.

    Article  Google Scholar 

  • Shanmathi, R., Nisha, R., & Samson, M. (2020). Spatial accessibility analysis of schools using geospatial techniques. Spatial Information Research, 28(6), 699–708. https://doi.org/10.1007/s41324-020-00326-w

    Article  Google Scholar 

  • Sue, T., Lisa, De B., & Catherine, U. (2017). PISA 2015: Reporting Australia's results (ACER Press, 2017), OECD Programme for International Student Assessment (PISA) Australia. https://research.acer.edu.au/ozpisa/22/

  • Sujan, P., Mahnas, J.M.-A., & John, E. B. (2022). Prediction of student academic performance using a hybrid 2D CNN model. Electronics, 11(7), 1005–1026.

    Article  Google Scholar 

  • Sunil, E., Nobuyuki, F., & Linda, S. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904.

    Article  Google Scholar 

  • Surjeet, K. Y., Brijesh, B., & Saurabh, P. (2012). Mining education data to predict student’s retention: A comparative study. International Journal of Computer Science and Information Security, 10(2), 113–117.

    Google Scholar 

  • AISHE Survey. (2015). National Data Sharing and Accessibility Policy. Ministry of Human Resource Development. https://data.gov.in/catalog/pupil-teacher-ratioaishe-survey?filters%5Bfield_catalog_reference%5D=258323&%0Aformat=json&offset=0&limit=6&sort%5Bcreated%5D=desc.%0A.

  • Trude, N., Jan-Eric, G., & Sigrid, B. (2016). Conceptual Framework and Methodology of This Report. Book Chapter. Teacher Quality, Instructional Quality and Student Outcomes Relationships Across Countries, Cohorts and Tim (Editors: Trude Nilsen and Jan-Eric Gustafsson), Springer Open, Volume 2, IEA Research for Education.

  • Ugbe, A. U., & Agim, J. I. (2009). Influence of teachers’ competence on students’ academic performance in senior secondary school chemistry. Global Journal of Educational Research, 18(1–2), 61–66.

    Google Scholar 

  • van der Emmelien, A., & Adrie J. V. (2018). Effects of data-based decision-making interventions for teachers on students’ mathematical achievement. Journal Teacher Education, 69(3), 307320.

    Google Scholar 

  • Vyacheslav, R., Yura, T., Natalya, B., & Valeriy, G. (2019). Monitoring student performance using computer technology. Slobozhanskyi Herald of Science and Sport, 7(2), 36–39.

    Google Scholar 

  • World Bank Report. (2021). Environmental and social management framework (ESMF) Draft Report. Meghalaya Health Systems Strengthening Project (P173589). https://projects.worldbank.org/en/projects-operations/project-detail/P173589.

  • Yang, L. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytic, 6(1), 1–29.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are highly thankful to the Department of Education, Government of Meghalaya for providing the secondary data and other associated supports from time to time for smoothly completion of work.

Author information

Authors and Affiliations

  1. Utah Water Research Laboratory, Utah State University, Logan, UT, USA

    Bushra Zaman

  2. Deepspatial Inc., Toronto, ON, Canada

    Bushra Zaman, Aisha Sharma, Chhotu Ram, Rahul Kushwah & Rajiv Muradia

  3. Directorate of School Education & Literacy, Department of Education, Government of Meghalaya, Shillong, Meghalaya, India

    Andrew Warjri, Dany K. Lyngdoh & Mark K. Lyngdoh

Authors
  1. Bushra Zaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aisha Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chhotu Ram
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rahul Kushwah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rajiv Muradia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andrew Warjri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dany K. Lyngdoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mark K. Lyngdoh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BZ, RM, and RK have developed the idea and design of the present work. The data extraction, collection, and analysis were performed by the AS, BZ, AW, DL, and MKL. CR and BZ wrote the initial draft of article and further revised and approved by the final draft by all the authors for the journal submission and publications.

Corresponding author

Correspondence to Chhotu Ram.

Ethics declarations

Competing interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval

The present study was jointly conducted by the Deepspatial Inc., Canada and Department of Education, Government of Meghalaya, India.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zaman, B., Sharma, A., Ram, C. et al. Modeling education impact: a machine learning-based approach for improving the quality of school education. J. Comput. Educ. (2023). https://doi.org/10.1007/s40692-023-00297-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40692-023-00297-5

Keywords

Search

Navigation