Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Square : Journal of Mathematics and Mathematics Education Journal of Integrated Elementary Education
Jimoh Kasali
Obafemi Awolowo University, Ile-Ife
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Model-Data Fit using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and The Sample-Size-Adjusted BIC Jimoh Kasali; Adediwura Alaba Adeyemi
Square : Journal of Mathematics and Mathematics Education Vol 4, No 1 (2022)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2022.4.1.11297

Abstract

The study determined if the 1PL, 2PL, 3PL and 4PL item response theory models best fit the data from the 2016 NECO Mathematics objective tests. Ex-post facto design was adopted for the study. The population for the study comprised 1,022,474 candidates who enrolled and sat for June/July SSCE 2016 NECO Mathematics Examination. The sample comprised 276,338 candidates who sat for the examination in three purposively Geo political zones in Nigeria (i.e., S/West, S/East and N/West). The research instruments used for the study were Optical Marks Record Sheets for the National Examination Council (NECO) June/July 2016 SSCE Mathematics objectives items. The responses of the tests were scored dichotomously. Data collected were analyzed using 2loglikelihood chi-square. The results of the likelihood ratio test revealed that 2PL fitted the data better than 1PL was statistically significant (χ2 (59) = 820636.1, p 0.05); the 2PL model fitted the data better than the 1PL model; 3PL model fitted the data better than the 2PL model and the result showed that the 4PL model fitted the data better than the 3PL model and the Likelihood ratio test that 4PL model fitted the data better than 3PL model was statistically significant, (χ2(60)=216159.2, p0.05). The study concluded that four-parameter logistic model fitted the 2016 NECO Mathematics test items.Keywords: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), one parameter model, two parameter model, three parameter model, four parameter model.
Estimation of Item Parameter Indices of NECO Mathematics Multiple Choice Test Items among Nigerian Students Jimoh Kasali; Adediwura Alaba Adeyemi
Journal of Integrated Elementary Education Vol. 2 No. 1 (2022)
Publisher : Universitas Islam Negeri Walisongo Semarang in collaboration with PD PGMI Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jieed.v2i1.10187

Abstract

The study estimated the difficulty, discrimination, and vulnerability to guessing 2016 National Examination Council (NECO) Mathematics multiple-choice test items. The study employed an ex-post facto design with 276,338 samples. The research instruments used for the study were Optical Marks Record Sheets for the NECO June/July 2016 Mathematics objectives items. The responses of the testees were scored dichotomously. Data collected were calibrated using four parameters logistic model. The results showed that most items in the 2016 NECO Mathematics test were good as their difficulty parameters were within (-2 to 2). For those items, difficulty parameter estimates are considered good. Also, the results indicated that only 21.7% of 2016 NECO Mathematics test items had a very good discriminating power, and the majority of the items had poor discrimination power. The result implies that most of the items were not effective in discriminating between examinees with the required ability and those that lack the required ability. Finally, the result revealed that the 2016 NECO test items were not vulnerable to guessing (i.e., 86.7% of items were good in terms of guessing). The study concluded that the 2016 NECO Mathematics test items were good for difficulty and guessing parameter indices.
Co-Authors Adediwura Alaba Adeyemi Adediwura Alaba Adeyemi