Implementing Computer Adaptive Testing for High-Stakes Assessment: A Shift for Examinations Council of Lesotho

Authors

  • Musa Adekunle Ayanwale National University of Lesotho Lesotho https://orcid.org/0000-0001-7640-9898
  • Julia Chere-Masopha National University of Lesotho Lesotho
  • Mapulane Mochekele National University of Lesotho Lesotho
  • Malebohang Catherine Morena National University of Lesotho Lesotho

DOI:

https://doi.org/10.24310/ijne.14.2024.20487

Keywords:

Assessment technology, computer adaptive testing (CAT), educational measurement, examination council of Lesotho, high-stakes assessments, item response theory

Abstract

We examine the feasibility of implementing Computer Adaptive Testing (CAT) for high-stakes assessments in Lesotho, specifically through the Examination Council of Lesotho (ECoL). CAT, a cutting-edge testing method, enhances precision and efficiency by adjusting test items in real-time based on an examinee's ability. While CAT has gained widespread global adoption, its implementation in developing countries presents significant challenges, particularly regarding infrastructure, expertise, and resource limitations. Our research addresses a critical gap in understanding how CAT can be effectively integrated into educational systems in developing contexts such as Lesotho. We conducted a literature review across five databases—Google Scholar, ERIC, PsycINFO, JSTOR, and PubMed—retrieving 48 studies published between 2013 and 2023. Of these, 18 studies met our inclusion criteria, focusing on CAT's advantages, challenges, and real-world applications in educational assessments. We applied thematic analysis to identify key benefits and barriers, focusing on precision, fairness, and infrastructure requirements. We outline the essential stages of CAT development, including feasibility studies, item bank creation, pretesting and calibration, specification determination, and live CAT publication. We highlight critical tasks such as using Monte Carlo simulations to validate CAT feasibility and developing a robust item bank calibrated with Item Response Theory (IRT). We also address challenges like building a robust technological infrastructure, providing comprehensive stakeholder training, and securing adequate funding. We emphasize the importance of continuous evaluation and stakeholder engagement to ensure CAT's successful implementation and sustainability. Global trends indicate growing adoption, driven by advancements in psychometrics and technology. CAT has the potential to offer more equitable and accurate assessments, making it a promising solution to improve educational outcomes in Lesotho.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Musa Adekunle Ayanwale, National University of Lesotho

Department of Educational Foundations, National University of Lesotho

Julia Chere-Masopha, National University of Lesotho

Department of Educational Foundations

Mapulane Mochekele, National University of Lesotho

Department of Educational Foundations, National University of Lesotho, Lesotho.

 

Malebohang Catherine Morena, National University of Lesotho

Department of Educational Foundations

References

Ayanwale, M. A., Chere-Masopha, J., & Morena, M. C. (2022). The Classical Test or Item Response Measurement Theory: The Status of the Framework at the Examination Council of Lesotho. International Journal of Learning, Teaching and Educational Research, 21(8), 384–406. https://doi.org/10.26803/IJLTER.21.8.22

Ayanwale, M. A., & Ndlovu, M. (2022). Transition from computer-based testing of national benchmark tests to adaptive testing: Robust application of fourth industrial revolution tools. Cypriot Journal of Educational Sciences, 17(9), 3327–3343. https://doi.org/10.18844/CJES.V17I9.7124

Han, K. C. T. (2018a). Components of the item selection algorithm in computerized adaptive testing. Journal of Educational Evaluation for Health Professions, 15, 7. https://doi.org/10.3352/JEEHP.2018.15.7

Han, K. C. T. (2018b). Conducting simulation studies for computerized adaptive testing using SimulCAT: an instructional piece. Journal of Educational Evaluation for Health Professions, 15, 20. https://doi.org/10.3352/jeehp.2018.15.20

Han, K. T. (2012). SimulCAT: Windows Software for Simulating Computerized Adaptive Test Administration. Applied Psychological Measurement, 36(1), 64–66. https://doi.org/10.1177/0146621611414407

Han, K. T. (2016). Maximum Likelihood Score Estimation Method With Fences for Short-Length Tests and Computerized Adaptive Tests. Applied Psychological Measurement, 40(4), 289–301. https://doi.org/10.1177/0146621616631317

Leroux, A. J., Lopez, M., Hembry, I., & Dodd, B. G. (2013). A Comparison of Exposure Control Procedures in CATs Using the 3PL Model. Educational and Psychological Measurement, 73(5), 857–874. https://doi.org/10.1177/0013164413486802

Meijer, R. R., & Nering, M. L. (n.d.). Computerized Adaptive Testing: Overview and Introduction.

Mujtaba, D. F., & Mahapatra, N. R. (2020). Artificial Intelligence in Computerized Adaptive Testing. Proceedings - 2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020, 649–654. https://doi.org/10.1109/CSCI51800.2020.00116

Ogunjimi, M. O., Ayanwale, M. A., Oladele, J. I., Daramola, D. S., Jimoh, I. M., & Owolabi, H. O. (2021). Simulated evidence of computer adaptive test length: Implications for high stakes assessment in Nigeria. Journal of Higher Education Theory and Practice, 21(2), 202–212. https://doi.org/10.33423/JHETP.V21I2.4129

Oladele, J. I., Ndlovu, M., & Spangenberg, E. D. (2022). Simulated computer adaptive testing method choices for ability estimation with empirical evidence. International Journal of Evaluation and Research in Education, 11(3), 1392–1399. https://doi.org/10.11591/IJERE.V11I3.21986

Oladele, J.I.; Ayanwale, M.A. ;Owolabi, H.O. (2020). Paradigm Shifts in Computer Adaptive Testing in Nigeria in Terms of Simulated Evidences. Journal of Social Science, 63(1–3), 9–20. https://doi.org/10.31901/24566756.2020/63.1-3.2264

Rice, N., Pêgo, J. M., Collares, C. F., Kisielewska, J., & Gale, T. (2022). The development and implementation of a computer adaptive progress test across European countries. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100083

Seo, D. G. (2017). Overview and current management of computerized adaptive testing in licensing/certification examinations. Journal of Educational Evaluation for Health Professions, 14, 17. https://doi.org/10.3352/JEEHP.2017.14.17

Stepanek, L., & Martinkova, P. (2020). Feasibility of computerized adaptive testing evaluated by Monte-Carlo and post-hoc simulations. Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, FedCSIS 2020, 359–367. https://doi.org/10.15439/2020F197

Thompson, N. (2023). Computerized Adaptive Testing (CAT): An Introduction. https://assess.com/computerized-adaptive-testing/ Access date: 11-04-2024

Thompson, N. A. (2009). Item selection in computerized classification testing. Educational and Psychological Measurement, 69(5), 778–793. https://doi.org/10.1177/0013164408324460

Thompson, N. A., & Weiss, D. J. (2009). Computerized and Adaptive Testing in Educational Assessment. In F. Scheuermann & J. Björnsson (Eds.), The Transition to Computer-Based Assessment: New Approaches to Skills Assessment and Implications for Large-scale Testing (pp. 127–133). European Commission.

Thompson, N. A., & Weiss, D. J. (2011). A framework for the development of computerized adaptive tests. Practical Assessment, Research and Evaluation, 16(1), 1–9.

Tsaousis, I., Sideridis, G. D., & AlGhamdi, H. M. (2021). Evaluating a Computerized Adaptive Testing Version of a Cognitive Ability Test Using a Simulation Study. Journal of Psychoeducational Assessment, 39(8), 954–968. https://doi.org/10.1177/07342829211027753

Viswanandhne, S., & Nandakumar, G. S. (2017). Computer-Based Adaptive Testing. 2(2), 686–691.

Wainer, H., Dorans, N. J., Flaugher, R., Green, B. F., & Mislevy, R. J. (2000). Computerized Adaptive Testing. Computerized Adaptive Testing. https://doi.org/10.4324/9781410605931/Computerized-Adaptive-Testing-Howard-Wainer-Neil-Dorans-Ronald-Flaugher-Bert-Green-Robert-Mislevy

Downloads

Published

2024-12-09

How to Cite

Ayanwale, M. A., Chere-Masopha, J., Mochekele, M., & Morena, M. C. (2024). Implementing Computer Adaptive Testing for High-Stakes Assessment: A Shift for Examinations Council of Lesotho. International Journal of New Education, (14). https://doi.org/10.24310/ijne.14.2024.20487

Issue

Section

ARTICLES