Decoding Student Insights: Analyzing Response Change in NAEP Mathematics Constructed Response Items

Authors

  • Congning Ni Vanderbilt University
  • Bhashithe Abeysinghe American Institutes for Research
  • Juanita Hicks American Institutes for Research

Keywords:

Response Change, Process Data, Constructed Response, Automated Scoring, Writing Behavior

Abstract

The National Assessment of Educational Progress (NAEP), often referred to as The Nation’s Report Card, offers a window into the state of U.S. K-12 education system. Since 2017, NAEP has transitioned to digital assessments, opening new research opportunities that were previously impossible. Process data tracks students’ interactions with the assessment and helps researchers explore students’ decision-making processes. Response change is a behavior that can be observed and analyzed with the help of process data. Typically, response change research focuses on multiple-choice items as response changes for those items is easily evident in process data. However, response change behavior, while well known, has not been analyzed in constructed response items to our knowledge. With this study we present a framework to conduct such analyses by presenting a dimensional schema to detect what kind of response changes students conduct and how they are related to student performance by integrating an automated scoring mechanism. Results show that students make changes to grammar, structure, and the meaning of their response. Results also revealed that while most students maintained their initial score across attempts, among those whose score did change, factor changes were more likely to improve scores compared to grammar or structure changes. Implications of this study show how we can combine automated item scoring with dimensional response changes to investigate how response change patterns may impact student performance.

Downloads

Download data is not yet available.

References

Al-Hamly, M., & Coombe, C. (2005). To change or not to change: Investigating the value of MCQ answer changing for Gulf Arab students. Language Testing, 22(4), 509–531.

Aninditya, A., Hasibuan, M. A., & Sutoyo, E. (2019). Text Mining Approach Using TF-IDF and Naive Bayes for Classification of Exam Questions Based on Cognitive Level of Bloom’s Taxonomy. 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), 112–117. https://doi.org/10.1109/IoTaIS47347.2019.8980428

Beck, M. D. (1978). The Effect of Item Response Changes on Scores on an Elementary Reading Achievement Test. The Journal of Educational Research, 71(3), 153–156. https://doi.org/10.1080/00220671.1978.10885059

Benjamin, L., Cavell, T., & Shallenberger, W. (1984). Staying with Initial Answers on Objective Tests: Is it a Myth? Teaching of Psychology, 11, 133–141. https://doi.org/10.1177/009862838401100303

Bergner, Y., & von Davier, A. A. (2019). Process Data in NAEP: Past, Present, and Future. Journal of Educational and Behavioral Statistics, 44(6), 706–732. https://doi.org/10.3102/1076998618784700

Bridgeman, B. (2012). A Simple Answer to a Simple Question on Changing Answers. Journal of Educational Measurement, 49(4), 467–468. https://doi.org/10.1111/j.1745-3984.2012.00189.x

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (arXiv:1810.04805). arXiv. https://doi.org/10.48550/arXiv.1810.04805

Engblom, C., Andersson, K., & Åkerlund, D. (2020). Young students making textual changes during digital writing. Nordic Journal of Digital Literacy, 15(3), 190–201. https://doi.org/10.18261/issn.1891-943x-2020-03-05

Ercikan, K., Guo, H., & He, Q. (2020). Use of Response Process Data to Inform Group Comparisons and Fairness Research. Educational Assessment, 25(3), 179–197. https://doi.org/10.1080/10627197.2020.1804353

Hojeij, Z., & Hurley, Z. (2017). The Triple Flip: Using Technology for Peer and Self-Editing of Writing. International Journal for the Scholarship of Teaching and Learning, 11(1). https://eric.ed.gov/?id=EJ1136125

Jeon, M., Boeck, P., & Linden, W. (2017). Modeling answer change behavior: An application of a generalized item response tree model. Journal of Educational and Behavioral Statistics, 42(4), 467–490.

Jeon, M., De Boeck, P., & van der Linden, W. (2017). Modeling answer change behavior: An application of a generalized item response tree model. Journal of Educational and Behavioral Statistics, 42(4), 467–490.

Johnson, E. G. (1992). The Design of the National Assessment of Educational Progress. Journal of Educational Measurement, 29(2), 95–110. https://doi.org/10.1111/j.1745-3984.1992.tb00369.x

Kim, H.-K., & Kim, H. A. (2022). Analysis of Student Responses to Constructed Response Items in the Science Assessment of Educational Achievement in South Korea. International Journal of Science & Mathematics Education, 20(5), 901–919. https://doi.org/10.1007/s10763-021-10198-7

Latif, E., & Zhai, X. (2024). Fine-tuning ChatGPT for automatic scoring. Computers and Education: Artificial Intelligence, 6, 100210. https://doi.org/10.1016/j.caeai.2024.100210

Lee, Y.-H., & Jia, Y. (2014). Using response time to investigate students’ test-taking behaviors in a NAEP computer-based study. Large-Scale Assessments in Education, 2(1), 8. https://doi.org/10.1186/s40536-014-0008-1

Linden, W. J. van der, & Jeon, M. (2012). Modeling Answer Changes on Test Items. Journal of Educational and Behavioral Statistics, 37(1), 180–199. https://doi.org/10.3102/1076998610396899.

Liu, O. L., Bridgeman, B., Gu, L., Xu, J., & Kong, N. (2015). Investigation of response changes in the GRE revised general test. Educational and Psychological Measurement, 75(6), 1002–1020.

Malekian, D., Bailey, J., Kennedy, G., de Barba, P., & Nawaz, S. (2019). Characterising Students’ Writing Processes Using Temporal Keystroke Analysis. International Educational Data Mining Society. https://eric.ed.gov/?id=ED599193

McMorris, R. F., & Others, A. (1991). Why Do Young Students Change Answers on Tests? https://eric.ed.gov/?id=ED342803

Morris, W., Holmes, L., Choi, J. S., & Crossley, S. (2024). Automated Scoring of Constructed Response Items in Math Assessment Using Large Language Models. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00418-w

Ouyang, W., Harik, P., Clauser, B. E., & Paniagua, M. A. (2019a). Investigation of answer changes on the USMLE® Step 2 Clinical Knowledge examination. BMC Medical Education, 19(1), 389. https://doi.org/10.1186/s12909-019-1816-3

Ouyang, W., Harik, P., Clauser, B. E., & Paniagua, M. A. (2019b). Investigation of Answer Changes on the USMLE® Step 2 Clinical Knowledge Examination. BMC Medical Education, 19(1), 389. https://doi.org/10.1186/s12909-019-1816-3.

Pools, E., & Monseur, C. (2021). Student test-taking effort in low-stakes assessments: Evidence from the English version of the PISA 2015 science test. Large-Scale Assessments in Education, 9(1), 10. https://doi.org/10.1186/s40536-021-00104-6

Qiao, X., & Hicks, J. (2020, August 11). Exploring Answer Change Behavior Using NAEP Process Data. AIR - Technical Memorandum.

Setzer, J. C., Wise, S. L., van den Heuvel, J. R., & Ling, G. (2013). An Investigation of Examinee Test-Taking Effort on a Large-Scale Assessment. Applied Measurement in Education, 26(1), 34–49. https://doi.org/10.1080/08957347.2013.739453

Tate, T. P., & Warschauer, M. (2019). Keypresses and Mouse Clicks: Analysis of the First National Computer-Based Writing Assessment. Technology, Knowledge and Learning, 24(4), 523–543. https://doi.org/10.1007/s10758-019-09412-x

Tiemann, G. (2015). An Investigation of Answer Changing on a Large-Scale Computer-Based Educational Assessment [(Doctoral dissertation,]. University of Kansas.

Tyack, L., Khorramdel, L., & von Davier, M. (2024). Using convolutional neural networks to automatically score eight TIMSS 2019 graphical response items. Computers and Education: Artificial Intelligence, 6, 100249. https://doi.org/10.1016/j.caeai.2024.100249

van der Linden, W. J., & Jeon, M. (2012). Modeling Answer Changes on Test Items. Journal of Educational and Behavioral Statistics, 37(1), 180–199. https://doi.org/10.3102/1076998610396899

Whitmer, J., Beiting-Parrish, M., Blankenship, C., Folwer-Dawson, A., & Pitcher, M. (2023). NAEP Math Item Automated Scoring Data Challenge Results: High Accuracy and Potential for Additional Insights.

Downloads

Published

2025-03-23

How to Cite

Ni, C., Abeysinghe, B., & Hicks, J. (2025). Decoding Student Insights: Analyzing Response Change in NAEP Mathematics Constructed Response Items. International Electronic Journal of Elementary Education, 17(2), 237–251. Retrieved from https://www.iejee.com/index.php/IEJEE/article/view/2395