Determination of Differential Functioning, Difficulty and Discriminating Indices and Students’ Performance in 2023 BECE Multiple-Choice Test
Abstract
The study investigated the rate of items response (IR), differential functioning (DF), of students’ performance, difficulty and discriminative indices of 2023, Mathematics multiple choice test used in Basic Education Certificate Examination (BECE) in Delta State. Four research questions guided the study which were answered using percentage, difficulty and discriminative indices formula. The instrument was 60 multiple-choice test items adopted from the 2023 BECE in Delta State. The population of the study comprised 140,959 upper basic three students from public secondary schools. The sample size of 1000 upper basic three students were drawn from the population using multi-stage sampling technique. The multi-triangulation research design was utilized. The instrument was administered to the students by the researcher and the researcher assistants. The study discovered the following findings: (1) The incidence of IR, DF issues in 2023 BECE test items was low with only about 5% of items indicating significant concern (2) The items have difficulty indices ranging from 0.31 to 0.55 and the estimated discriminative indices ranged from 0.34 to 0.67 and (3) There were similarity in performance of male and female students. Based on these findings the following recommendations were made among others: teachers should identify the items response, differential functioning in order to establish the psychometric properties of test items designed by them.
Full Text:
PDFReferences
Anisah, N., Suparno, S., & Anwar, Y. (2022). Evaluation of test items using the four-parameter logistic model: A case study of multiple-choice questions. International Journal of Instruction, 15(3), 231–246. https://doi.org/10.29333/iji.2022.15313a
Bello, T. A., & Omisore, M. A. (2023). Curriculum alignment and test item performance in standardized assessments: A Nigerian context. African Journal of Educational Research and Development, 15(1), 51–66. https://doi.org/10.4314/
Cheng, Y., & Liu, C. (2015). The Effect of Upper and Lower Asymptotes of IRT Models on Computerized Adaptive Testing. Applied Psychological Measurement, 39(7), 551–565. https://doi.org/10.1177/0146621615585850
Guo, S., & Ercikan, K. (2020). Test engagement and rapid guessing: Evidence from a large-scale state assessment. Frontiers in Education, 8, 1127644. https://doi.org/10.3389/feduc.2023.1127644
Hamza, S. A., & Hussaini, A. (2023). Items Analysis of Mathematics Basic Education Certificate Examination in Junior Secondary Schools before and After Covid-19 Lockdown in Sokoto State. African Journal of Humanities and Contemporary Education Research, 11(1), 243–257. Retrieved from https://publications.afropolitanjournals.com/index.php/ajhcer/article/view/474
Issayeva, L. (2024). Factor Analysis: Evaluating Dimensionality in Assessment. SAGE Publishing.
Jessa, M. O., Odili, J. N., & Osadebe, P. U. (2023). Development of Social Studies Aptitude Test for Testing Critical Thinking Skills: Implication for the Achievement of Education for Sustainable Development (ESD). Canadian Journal of Educational and Social Studies, 3(4), 99-119. DOI: 10.53103/cjess.v3i4.163
Johnson, L., & Taylor, A. (2024). Convergent Evidence of Construct Validity: New Perspectives. Educational Research Review, 19(2), 112-126. https://doi.org/10.1016/j.edurev.2024.100212
Johnson, L., Smith, R., & Lee, R. (2023). Exploring Latent Space in Educational Assessments. Educational and Psychological Measurement, 82(2), 75-89. https://doi.org/10.1177/0013164422111456
Kose, S. (2023). Evaluating model-data fit in IRT: A stepwise approach. Educational Research Review, 18(2), 123-135. https://doi.org/10.1016/j.edurev.2023.100123
Kpolovie, P.J. and Emekene, C.O. (2016). Item Response Theory Validation of Advanced Progressive Matrices in Nigeria. British Journal of Psychology Research 4(1) 1-32
Lawal, A. (2019). Assessing Senior Secondary School Students’ Mathematical Proficiency as Related to Gender and Performance in Mathematics in Nigeria. International Journal of Research in Education and Science (IJRES), 3(2), 488-502.
Miller, J., & Green, T. (2024). Understanding the Standard Error of Measurement in Psychological Assessments. Psychological Assessment, 39(1), 55-70. https://doi.org/10.1037/pas0000486
Miller, J., & Thompson, R. (2024). Understanding Face Validity: Implications for Test Design. Psychological Assessment, 36(4), 330-345. https://doi.org/10.1037/pas0000462
National Council of Teachers of Mathematics. (2020). Catalyzing change in high school mathematics: Initiating the conversation on a pathway to equity and excellence. https://www.nctm.org/Standards-and-Positions/Principles-and-Standards/
Nwachukwu, T. (2023). Secondary School Students Self-Regulated Learning Skill as Predictor of Mathematics Achievement in Imo State Nigeria: Focus on Gender. Brillo Journal, 2(2), 94-107.
Odu, P. (2025). Evaluation of the implementation of Junior Secondary School Mathematics Curriculum for Future Resilient Society Renewal in Delta State. Irish Journal of Education Practice; 8, Issue 04, 43-53.
Omoruan, B. E. (2017). Construction, validation and application of mathematics language mastery achievement test for junior secondary school students in Delta and Edo States, Nigeria. Unpublished Doctoral Thesis, University of Port Harcourt, Port Harcourt.
Opara, I.M. (2016). Test construction and measurement: Concept and applications. Owerri: Career Publishers.
Orheruata, M. U. (2015). Item Parameter Drift in Certificate Examinations and it’s implication on Decision Making. African Journal of Theory and Practice of Educational assessment. Educational Assessment 2, 98-105.
Pill, J. (2021). Content Validation and Its Role in Ensuring Test Accuracy. Educational Research Review, 16(1), 45-62. https://doi.org/10.1016/j.edurev.2021.100109
Robitzsch, A. (2022). Four-Parameter Guessing Model and Related Item Response Models. Mathematical and Computational Applications, 27(6), 95. https://doi.org/10.3390/mca27060095
Smith, R., & Brown, T. (2024). The Role of SEM in Test Reliability. Educational and Psychological Measurement, 88(1), 90-105. https://doi.org/10.1177/0013164423112349
Thompson, N. J. (2019). Item response theory: Modelling psychometric data (2nd ed.). Routledge.
Traub, R. E. (2020). Psychometric theory and applications. Springer.
Refbacks
- There are currently no refbacks.