Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2023 / Volume: 14 Issue: 4

Comparing Estimated and Real Item Difficulty Using Multi-Facet Rasch Analysis

Ayfer Sayın(GAZI UNIVERSITY, GAZİ FACULTY OF EDUCATION)0000-0003-1357-5674
Sebahat Gören(HACETTEPE UNIVERSITY)0000-0002-6453-3258
Pages
—
Publication Date
2023-12-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However, they tended to underestimate item difficulty for items categorized as medium-easy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aimed to compare estimated item difficulty based on expert opinion with real item difficulty based on data.
📊
Key Findings & Data:For security reasons, some high-stakes tests are not pre-tested and item difficulty is estimated by teachers in classroom assessments, so it is necessary to examine the extent to which experts make accurate predictions.
💡
Conclusion & Impact:However, they tended to underestimate item difficulty for items categorized as medium-easy.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:test development · item difficulty · subject matter experts

Abstract

This study aimed to compare estimated item difficulty based on expert opinion with real item difficulty based on data. For security reasons, some high-stakes tests are not pre-tested and item difficulty is estimated by teachers in classroom assessments, so it is necessary to examine the extent to which experts make accurate predictions. In this study, we developed a 12-item assessment test like the Turkish teacher certification exam. Item difficulty was estimated and compared separately based on 1165 student responses and the opinions of 12 experts. The study revealed that the experts had a good ability to estimate item difficulty for items of moderate difficulty. However, they tended to underestimate item difficulty for items categorized as medium-easy.

🔖

Cite This Article

Sayın, A., Gören, S. (2023). Comparing Estimated and Real Item Difficulty Using Multi-Facet Rasch Analysis. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Volume: 14 Issue: 4. https://doi.org/10.21031/epod.1310893

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (39)

  1. Afrashteh, M. Y. (2021). Comparison of the validity of bookmark and Angoff standard setting methods in medical performance tests. Bmc Medical Education, 21(1). https://doi.org/10.1186/s12909-020-02436-3
  2. AITSL, A. I. f. T. a. S. L. (2022). AITSL, Australian Professional Standards for Teachers. https://www.aitsl.edu.au/tools-resources/resource/australian-professional-standards-for-teachers
  3. Attali, Y., Saldivia, L., Jackson, C., Schuppan, F., & Wanamaker, W. (2014). Estimating item difficulty with comparative judgments. ETS Research Report Series, 2014(2), 1-8. http://dx.doi.org/10.1002/ets2.12042
  4. Beinborn, L., Zesch, T., & Gurevych, I. (2014). Predicting the difficulty of language proficiency tests. Transactions of the Association for Computational Linguistics, 2, 517-530. https://doi.org/10.1162/tacl_a_00200
  5. Chon, Y. V., & Shin, T. (2010). Item difficulty predictors of a multiple-choice reading test. English Teaching, 65(4), 257-282. http://journal.kate.or.kr/wp-content/uploads/2015/02/kate_65_4_11.pdf
  6. Clauser, J. C., Hambleton, R. K., & Baldwin, P. (2017). The Effect of Rating Unfamiliar Items on Angoff Passing Scores.Educational and Psychological Measurement, 77(6), 901-916. https://doi.org/10.1177/0013164416670983
  7. Ell, F. (2021). Teacher education policy in Aotearoa New Zealand: Global trends meet local imperatives. In Teacher Education Policy and Research: Global Perspectives (pp. 113-128). Springer.
  8. Enright, M. K., Allen, N., & Kim, M. I. (1993). A Complexity Analysis of Items from a Survey of Academic Achievement in the Life Sciences. ETS Research Report Series, 1993(1), i-32. https://files.eric.ed.gov/fulltext/ED385595.pdf
  9. Fergadiotis, G., Swiderski, A., & Hula, W. D. (2019). Predicting confrontation naming item difficulty. Aphasiology, 33(6), 689-709. https://doi.org/10.1080/02687038.2018.1495310
  10. Gitomer, D. H., & Qi, Y. (2010). Recent Trends in Mean Scores and Characteristics of Test-Takers on" Praxis II" Licensure Tests. Office of Planning, Evaluation and Policy Development, US Department of Education.
  11. Grivokostopoulou, F., Hatzilygeroudis, I., & Perikos, I. (2014). Teaching assistance and automatic difficulty estimation in converting first order logic to clause form. Artificial Intelligence Review, 42, 347-367. http://dx.doi.org/10.1007/s10462-013-9417-8
  12. Hamamoto Filho, P. T., Silva, E., Ribeiro, Z. M. T., Hafner, M. d. L. M. B., Cecilio-Fernandes, D., & Bicudo, A. M. (2020). Relationships between Bloom’s taxonomy, judges’ estimation of item difficulty and psychometric properties of items from a progress test: a prospective observational study. Sao Paulo Medical Journal, 138, 33-39. http://dx.doi.org/10.1590/1516-3180.2019.0459.R1.19112019
  13. He, J., Peng, L., Sun, B., Yu, L. J., & Zhang, Y. H. (2021). Automatically Predict Question Difficulty for Reading Comprehension Exercises. 2021 Ieee 33rd International Conference on Tools with Artificial Intelligence (Ictai 2021), 1398-1402. https://doi.org/10.1109/Ictai52525.2021.00222
  14. Impara, J. C., & Plake, B. S. (1998). Teachers' ability to estimate item difficulty: A test of the assumptions in the Angoff standard setting method. Journal of Educational Measurement, 35(1), 69-81. https://psycnet.apa.org/doi/10.1111/j.1745-3984.1998.tb00528.x
  15. Kardong-Edgren, S., & Mulcock, P. M. (2016). Angoff Method of Setting Cut Scores for High-Stakes Testing Foley Catheter Checkoff as an Exemplar. Nurse Educator, 41(2), 80-82. https://doi.org/10.1097/Nne.0000000000000218
  16. Kibble, J. D., & Johnson, T. (2011). Are faculty predictions or item taxonomies useful for estimating the outcome of multiple-choice examinations? Advances in physiology education, 35(4), 396-401. https://doi.org/10.1152/advan.00062.2011
  17. Kurdi, G., Leo, J., Matentzoglu, N., Parsia, B., Sattler, U., Forge, S., Donato, G., & Dowling, W. (2021). A comparative study of methods for a priori prediction of MCQ difficulty. Semantic Web, 12(3), 449-465. https://doi.org/10.3233/Sw-200390
  18. Le Hebel, F., Tiberghien, A., Montpied, P., & Fontanieu, V. (2019). Teacher prediction of student difficulties while solving a science inquiry task: example of PISA science items. International Journal of Science Education, 41(11), 1517-1540. https://doi.org/10.1080/09500693.2019.1615150
  19. Lin, C.-S., Lu, Y.-L., & Lien, C.-J. (2021). Association between Test Item's Length, Difficulty, and Students' Perceptions: Machine Learning in Schools' Term Examinations. Universal Journal of Educational Research, 9(6), 1323-1332. http://dx.doi.org/10.13189/ujer.2021.090622
  20. Lin, L. H., Chang, T. H., & Hsu, F. Y. (2019). Automated Prediction of Item Difficulty in Reading Comprehension Using Long Short-Term Memory. Proceedings of the 2019 International Conference on Asian Language Processing (IALP), Shanghai, China, 132-135. https://doi.org/10.1109/IALP48816.2019.9037716.
  21. Linacre, J.M. (2014). A user's guide to FACETS Rasch-model computer programs. Retrieved from http://www.winsteps.com/a/facets-manual.pdf
  22. Lumley, T., Routitsky, A., Mendelovits, J., & Ramalingam, D. (2012). A framework for predicting item difficulty in reading tests.
  23. OSYM. (2022). KPSS: Kamu Personel Seçme Sınavı. https://www.osym.gov.tr/TR,23892/2022-kpss-lisans-genel-yetenek-genel-kultur-ve-egitim-bilimleri-oturumlarinin-temel-soru-kitapciklari-ve-cevap-anahtarlari-yayimlandi-31072022.html
  24. Pandarova, I., Schmidt, T., Hartig, J., Boubekki, A., Jones, R. D., & Brefeld, U. (2019). Predicting the difficulty of exercise items for dynamic difficulty adaptation in adaptive language tutoring. International Journal of Artificial Intelligence in Education, 29, 342-367. https://doi.org/10.1007/s40593-019-00180-4
  25. Perikos, I., Grivokostopoulou, F., Kovas, K., & Hatzilygeroudis, I. (2016). Automatic estimation of exercises' item difficulty in a tutoring system for teaching the conversion of natural language into first‐order logic. Expert Systems, 33(6), 569-580. https://doi.org/10.1111/exsy.12182
  26. Perkins, K., Gupta, L., & Tammana, R. (1995). Predicting item difficulty in a reading comprehension test with an artificial neural network. Language testing, 12(1), 34-53.https://doi.org/10.1177/026553229501200103
  27. Praxis, E. T. S. (2022). ETS, The Praxis Tests. https://www.ets.org/praxis
  28. Qiu, Z. P., Wu, X., & Fan, W. (2019). Question difficulty prediction for multiple choice problems in medical exams. Proceedings of the 28th Acm International Conference on Information & Knowledge Management (Cikm '19), 139-148. https://doi.org/10.1145/3357384.3358013
  29. Sano, M. (2015). Automated capturing of psycho-linguistic features in reading assessment text. Annual meeting of the National Council on Measurement in Education, Chicago, IL,
  30. Schult, J., & Lindner, M. A. (2018). Judgment Accuracy of German Elementary School Teachers: A Matter of Response Formats? German Journal of Educational Psychology, 32(1-2), 75-87. https://doi.org/10.1024/1010-0652/a000216
  31. Stadler, M., Niepel, C., & Greiff, S. (2016). Easily too difficult: Estimating item difficulty in computer simulated microworlds. Computers in Human Behavior, 65, 100-106. https://doi.org/10.1016/j.chb.2016.08.025
  32. Sydorenko, T. (2011). Item writer judgments of item difficulty versus real item difficulty: A case study. Language Assessment Quarterly, 8(1), 34-52. https://doi.org/10.1080/15434303.2010.536924
  33. Toyama, Y. (2021). What makes reading difficult? An Investigation of the contributions of passage, task, and reader characteristics on comprehension performance. Reading Research Quarterly, 56(4), 633-642. https://doi.org/10.1002/rrq.440
  34. Urhahne, D., & Wijnia, L. (2021). A review on the accuracy of teacher judgments. Educational Research Review, 32, 100374. https://doi.org/10.1016/j.edurev.2020.100374
  35. Wauters, K., Desmet, P., & Van Den Noortgate, W. (2012). Item difficulty estimation: An auspicious collaboration between data and judgment. Computers & Education, 58(4), 1183-1193. https://doi.org/10.1016/j.compedu.2011.11.020
  36. Wyse, A. E. (2018). Equating angoff standard-setting ratings with the rasch model.Measurement-Interdisciplinary Research and Perspectives, 16(3), 181-194. https://doi.org/10.1080/15366367.2018.1483170
  37. Wyse, A. E. (2020). Comparing cut scores from the angoff method and two variations of the hofstee and beuk methods. Applied Measurement in Education, 33(2), 159-173. https://doi.org/10.1080/08957347.2020.1732385
  38. Yaneva, V., Ha, L. A., Baldwin, P., & Mee, J. (2020, May). Predicting item survival for multiple choice questions in a high-stakes medical exam. Proceedings of the 12th International Conference on Language Resources and Evaluation (Lrec), 6812-6818. Marseille, France. https://aclanthology.org/2020.lrec-1.841.pdf
  39. Yim, M. K., & Shin, S. J. (2020). Using the Angoff method to set a standard on mock exams for the Korean Nursing Licensing Examination. Journal of Educational Evaluation for Health Professions, 17(4). https://doi.org/10.3352/jeehp.2020.17.14