Entropy-Weight Derivation for Ranking STEM–CBE Preparedness Indicators in Public Senior Secondary Schools: Evidence from Bungoma County, Kenya

References

[1] UNESCO, Education for Sustainable Development Goals: Learning Objectives, United Nations Educational, Scientific and Cultural Organization, Paris, 2017.

[2] J. Ngure, From compliance to curiosity: How Kenya’s shift to CBE can redefine learning, Women Educational Researchers of Kenya (WERK), June 2025.

[3] Republic of Kenya, Ministry of Education, Sessional Paper on Reforming Education and Training for Sustainable Development in Kenya, Government of Kenya, Nairobi, 2024.

[4] M. D. Sagwa, S. Odebero, and J. Nganyi, Projection models for selected infrastructural requirements for the implementation of competency-based curriculum in senior secondary schools in 2026, in Kenya, International Journal of Education and Research 12 (2024), no. 11.

[5] Kenya Institute of Curriculum Development, National Readiness Assessment for Junior Secondary School Implementation, KICD, Nairobi, 2021.

[6] C.-H. Chen, A novel multi-criteria decision-making model for building material supplier selection based on entropy-AHP weighted TOPSIS, Entropy 22 (2020), no. 2, 259.

[7] C. E. Shannon, A mathematical theory of communication, Bell System Technical Journal 27 (1948), no. 3, 379–423.

[8] M. Zeleny, Multiple Criteria Decision Making, McGraw-Hill, New York, 1982.

[9] C. L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin, 1981.

[10] C. Rohlfsen, K. Shannon, and A. S. Parsons, Entropy in clinical decision-making: A narrative review through the lens of decision theory, Journal of General Internal Medicine 40 (2025), no. 16, 4033–4039.

[11] L. Xia, Y. Chen, and H. Zhang, Clustering methods for educational resource evaluation in primary schools: A case study of Guangdong Province, Journal of Educational Data Mining 14 (2022), no. 2, 87–109.

[12] T. Xiao, R. Wang, and J. Liu, Evaluating facility readiness in Tibetan secondary schools using fuzzy c-means clustering, International Journal of Educational Research 113 (2022), 101936.

[13] E. Shitindi, School readiness for competency-based curriculum in Tanzania’s secondary schools, African Educational Research Journal 9 (2021), no. 1, 1–11.

[14] T. Mugiraneza and J. F. Maniraho, Teacher preparedness for competency-based curriculum implementation in Rwandan secondary schools, African Journal of Education and Practice 6 (2020), no. 3, 1–14.

[15] J. O. Otieno, Assessment of physical infrastructure readiness for STEM pathway delivery in Kenyan secondary schools, East African Journal of Education 7 (2020), no. 2, 23–38.

[16] N. W. Wafula, Competency Based Curriculum Infrastructure Readiness in Trans-Nzoia County Secondary Schools: A Cross-Sectional Study, Master’s thesis, Kibabii University, 2022.

[17] Presidential Working Party on Education Reform, Report of the Presidential Working Party on Education Reform: Transforming Education, Training and Research for Sustainable Development in Kenya, Republic of Kenya, 2023.

[18] T. Yamane, Statistics: An Introductory Analysis, 2nd ed., Harper and Row, New York, 1967.

[19] P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 20 (1987), 53–65.

[20] R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2024.

@article{1719671,
      author = {Yasiro Carren Mutua, Moses Kololi, Jacinta Mutwiwa},
      title = {Entropy-Weight Derivation for Ranking STEM–CBE Preparedness Indicators in Public Senior Secondary Schools: Evidence from Bungoma County, Kenya},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {1007-1015},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719671.pdf},
      abstract = {The rollout of Competency Based Education (CBE) in Kenya’s senior secondary schools has created a need for objective, data-driven tools to monitor school readiness for the Science, Technology, Engineering and Mathematics (STEM) pathway. School preparedness is multidimensional, yet most evaluation schemes treat its indicators as equally important, which overweights indicators that carry little discriminating information and underweights those that separate schools most sharply. This paper derives objective weights for twelve STEM–CBE preparedness indicators using Shannon entropy, as a preliminary step to entropy-weighted clustering. Data were collected from 112 public senior secondary schools across the nine sub-counties of Bungoma County through structured questionnaires and facility observation checklists under stratified random sampling. After min–max normalisation, the informational contribution of each indicator was quantified through its normalised Shannon entropy, converted to a degree of diversification, and normalised to a unit-sum weight vector. The derived weights ranged from 0.022 to 0.141—a spread of roughly six-fold—and summed to unity. ICT device provision (weight 0.141) and ICT-integrated lesson frequency (0.119) were the two sharpest markers of preparedness, and together with stakeholder engagement (0.112) and the presence of a STEM strategic plan and budget (0.104) the ICT and institutional-leadership indicators carried about 47.6 percent of the total informational value. Student competency in STEM tasks (0.022), practical lesson frequency (0.041) and STEM teacher density (0.052) were least informative, reflecting limited variation across schools rather than low substantive importance. The ranking identifies the digital divide and governance capacity as the conditions that most sharply differentiate senior-school readiness in the county and supplies a defensible, reproducible weight vector for downstream profiling.},
      keywords = {Entropy Weighting, Shannon Entropy, STEM–CBE Preparedness, Indicator Ranking},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1719671}
  }