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Examination of response time effort in TIMSS 2019: Comparison of Singapore and Türkiye

Year 2023, , 174 - 193, 27.12.2023
https://doi.org/10.21449/ijate.1343248

Abstract

In this paper, it is aimed to evaluate different aspects of students' response time to items in the mathematics test and their test effort as an indicator of test motivation with the help of some variables at the item and student levels. The data consists of 4th-grade Singapore and Turkish students participating in the TIMSS 2019. Response time was examined in terms of item difficulties, content and cognitive domains of the items in the mathematics test self-efficacy for computer use, home resources for learning, confident in mathematics, like learning mathematics, and gender variables at the student level. In the study, it was determined that all variables considered at the item level affected the response time of the students in both countries. It was concluded that the amount of variance explained by the student-level variables in the response time varied for each the country. Another finding of the study showed that the cognitive level of the items positively related to the mean response time. Both Turkish and Singaporean students took longer to respond to data domain items compared to number and measurement and geometry domain items. Additionally, based on the criterion that the response time effort index was less than .8, rapid-guessing behavior, and therefore low motivation, was observed below 1% for both samples. Besides, we observed that Turkish and Singaporean students were likely to have rapid guessing behavior when an item in the reasoning domain became increasingly difficult. A similar result was identified in the data content domain, especially for Turkish graders.

References

  • American Psychological Association. (2022). Self-report bias. In APA dictionary of psychology. https://dictionary.apa.org/self-report-bias
  • Barry, C.L, & Finney, S.J. (2009). Exploring change in test-taking motivation. Northeastern Educational Research Association
  • Barry, C.L., Horst, S.J., Finney, S.J., Brown, A.R., & Kopp, J.P. (2010). Do examinees have similar test-taking effort? A high-stakes question for low-stakes testing. International Journal of Testing, 10, 342–363. https://doi.org/10.1080/15305058.2010.508569
  • Baumert, J., & Demmrich, A. (2001). Test motivation in the assessment of student skills: the effects of incentives on motivation and performance. European Journal of Psychology of Education, 14, 441–462. http://www.jstor.org/stable/23420343
  • Bennett, R.E., Brasell, J., Oranje, A., Sandene, B., Kaplan, K., & Yan, F. (2008). Does it matter if I take my mathematics test on a computer? A second empirical study of mode effects in NAEP. Journal of Technology, Learning, and Assessment, 6(9), 1 39. https://files.eric.ed.gov/fulltext/EJ838621.pdf
  • Bergstrom, B.A., Gershon, R.C., & Lunz, M.E. (1994, April 4-8). Computer adaptive testing: Exploring examinee response time using hierarchical linear modeling. Paper presented at the annual meeting of the National Council on Measurement in Education, New Orleans, LA. https://files.eric.ed.gov/fulltext/ED400287.pdf
  • Borgonovi, F., Ferrara, A., & Piacentini, M. (2021). Performance decline in a low-stakes test at age 15 and educational attainment at age 25: Cross-country longitudinal evidence. Journal of Adolescence, 92, 114-125. https://doi.org/10.1016/j.adolescence.2021.08.011
  • Bridgeman, B., & Cline, F. (2000). Variations in mean response time for questions on the computer-adaptive GRE General Test: Implications for fair assessment. GRE Board Professional Report No. 96 20P. Educational Testing Service. https://doi.org/10.1002/j.2333-8504.2000.tb01830.x
  • Chae, Y.M., Park, S.G., & Park, I. (2019). The relationship between classical item characteristics and item response time on computer-based testing. Korean Journal of Medical Education, 31(1), 1-9. https://doi.org/10.3946/kjme.2019.113
  • Chen, G., Cheng, W., Chang, T.W., Zheng, X., & Huang, R. (2014). A comparison of reading comprehension across paper, computer screens, and tablets: Does tablet familiarity matter? Journal of Computers in Education, 1(3), 213 225. http://dx.doi.org/10.1007%2Fs40692-014-0012-z
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Cole, J.S., Bergin, D.A., & Whittaker, T.A. (2008). Predicting student achievement for low stakes tests with effort and task value. Contemporary Educational Psychology, 33(4), 609–624. https://doi.org/10.1016/j.cedpsych.2007.10.002
  • Cooper, J. (2006). The digital divide: The special case of gender. Journal of Computer Assisted Learning, 22, 320–334. https://doi.org/10.1111/j.1365-2729.2006.00185.x
  • Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Wadsworth.
  • Çokluk, Ö., Gül, E., & Doğan-Gül, C. (2016). Examining differential item functions of different item ordered test forms according to item difficulty levels. Educational Sciences-Theory & Practice, 16(1), 319-330. https://doi.org/10.12738/estp.2016.1.0329
  • DeMars, C.E. (2007). Changes in rapid-guessing behavior over a series of assessments. Educational Assessment, 12(1), 23–45. https://doi.org/10.1080/10627190709336946
  • Eklöf, H. (2007). Test-taking motivation and mathematics performance in the TIMSS 2003. International Journal of Testing, 7(3), 311 326. https://doi.org/10.1080/15305050701438074
  • Eklöf, H. (2010). Skill and will: Test-taking motivation and assessment quality. Assessment in Education Principles Policy Practice, 17, 345 356. https://doi.org/10.1080/0969594X.2010.516569
  • Fan, Z., Wang, C., Chang, H.-H., & Douglas, J. (2012). Response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37(5), 655-670. http://dx.doi.org/10.3102/1076998611422912
  • Fishbein, B., Foy, P., & Yin, L. (2021). TIMSS 2019 user guide for the international database (2nd ed.). TIMSS & PIRLS International Study Center.
  • Gneezy, U., List, J.A., Livingston, J.A., Qin, X., Sadoff, S., & Xu, Y. (2019). Measuring success in education: the role of effort on the test itself. American Economic Review: Insights, 1(3), 291-308. http://dx.doi.org/10.1257/aeri.20180633
  • Guo, H., Rios, J.A., Haberman, S., Liu, O.L., Wang, J., & Paek, I. (2016). A new procedure for detection of students’ rapid guessing responses using response time. Applied Measurement in Education, 29(3), 173 183. https://doi.org/10.1080/08957347.2016.1171766
  • Hannula. (2004). Development of understanding and self-confidence in mathematics, grades 5-8. Proceding of the 28th Conference of the International Group for the Psychology of Mathematics Education, 3, 17-24. http://files.eric.ed.gov/fulltext/ED489565.pdf
  • Hess, B.J., Johnston, M.M., & Lipner, R.S. (2013). The impact of item format and examinee characteristics on response times. International Journal of Testing, 13(4), 295–313. https://doi.org/10.1080/15305058.2012.760098
  • Hoffman, B. (2010). “I think I can, but I'm afraid to try”: The role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency. Learning and Individual Differences, 20(3), 276-283. https://doi.org/10.1016/j.lindif.2010.02.001
  • Hoffman, B., & Spatariu, A. (2008). The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary Educational Psychology, 33(4), 875-893. https://doi.org/10.1016/j.cedpsych.2007.07.002
  • İlgün-Dibek, M. (2020). Silent predictors of test disengagement in PIAAC 2012. Journal of Measurement and Evaluation in Education and Psychology, 11(4), 430-450. https://doi.org/10.21031/epod.796626
  • İlhan, M., Öztürk, N.B., & Şahin, M.G. (2020). The effect of the item’s type and cognitive level on its difficulty index: The sample of the TIMSS 2015. Participatory Educational Research, 7(2), 47-59. https://doi.org/10.17275/per.20.19.7.2
  • Koçdar, S., Karadağ, N., & Şahin, M.D. (2016). Analysis of the difficulty and discrimination indices of multiple-choice questions according to cognitive levels in an open and distance learning context. The Turkish Online Journal of Educational Technology, 15(4), 16–24. https://hdl.handle.net/11421/11442
  • Lasry, N., Watkins, J., Mazur, E., & Ibrahim, A. (2013). Response times to conceptual questions. American Journal of Physics, 81(9), 703 706. https://doi.org/10.1119/1.4812583
  • Lee, Y.H., & Chen, H. (2011). A review of recent response-time analyses in educational testing. Psychological Test and Assessment Modeling, 53(3), 359–379.
  • 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(8), 1-24. https://doi.org/10.1186/s40536-014-0008-1
  • Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: A causal analysis. Computers in Human Behavior, 14(1), 125 146. http://dx.doi.org/10.1016/0747-5632(93)90033-O
  • Lundgren, E., & Eklöf, H. (2020). Within-item response processes as indicators of test-taking effort and motivation. Educational Research and Evaluation, 26(5-6), 275-301. https://doi.org/10.1080/13803611.2021.1963940
  • Martin, M.O., von Davier, M., & Mullis, I.V.S. (Eds.). (2020). Methods and procedures: The TIMSS 2019 technical report. The TIMSS & PIRLS International Study Center. https://www.iea.nl/publications/technical-reports/methods-and-procedures-timss-2019-technical-report
  • Michaelides, M.P., Ivanova, M., & Nicolaou, C. (2020). The relationship between response-time effort and accuracy in PISA science multiple choice items. International Journal of Testing, 20(3), 187-205. https://doi.org/10.1080/15305058.2019.1706529
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Examination of response time effort in TIMSS 2019: Comparison of Singapore and Türkiye

Year 2023, , 174 - 193, 27.12.2023
https://doi.org/10.21449/ijate.1343248

Abstract

In this paper, it is aimed to evaluate different aspects of students' response time to items in the mathematics test and their test effort as an indicator of test motivation with the help of some variables at the item and student levels. The data consists of 4th-grade Singapore and Turkish students participating in the TIMSS 2019. Response time was examined in terms of item difficulties, content and cognitive domains of the items in the mathematics test self-efficacy for computer use, home resources for learning, confident in mathematics, like learning mathematics, and gender variables at the student level. In the study, it was determined that all variables considered at the item level affected the response time of the students in both countries. It was concluded that the amount of variance explained by the student-level variables in the response time varied for each the country. Another finding of the study showed that the cognitive level of the items positively related to the mean response time. Both Turkish and Singaporean students took longer to respond to data domain items compared to number and measurement and geometry domain items. Additionally, based on the criterion that the response time effort index was less than .8, rapid-guessing behavior, and therefore low motivation, was observed below 1% for both samples. Besides, we observed that Turkish and Singaporean students were likely to have rapid guessing behavior when an item in the reasoning domain became increasingly difficult. A similar result was identified in the data content domain, especially for Turkish graders.

References

  • American Psychological Association. (2022). Self-report bias. In APA dictionary of psychology. https://dictionary.apa.org/self-report-bias
  • Barry, C.L, & Finney, S.J. (2009). Exploring change in test-taking motivation. Northeastern Educational Research Association
  • Barry, C.L., Horst, S.J., Finney, S.J., Brown, A.R., & Kopp, J.P. (2010). Do examinees have similar test-taking effort? A high-stakes question for low-stakes testing. International Journal of Testing, 10, 342–363. https://doi.org/10.1080/15305058.2010.508569
  • Baumert, J., & Demmrich, A. (2001). Test motivation in the assessment of student skills: the effects of incentives on motivation and performance. European Journal of Psychology of Education, 14, 441–462. http://www.jstor.org/stable/23420343
  • Bennett, R.E., Brasell, J., Oranje, A., Sandene, B., Kaplan, K., & Yan, F. (2008). Does it matter if I take my mathematics test on a computer? A second empirical study of mode effects in NAEP. Journal of Technology, Learning, and Assessment, 6(9), 1 39. https://files.eric.ed.gov/fulltext/EJ838621.pdf
  • Bergstrom, B.A., Gershon, R.C., & Lunz, M.E. (1994, April 4-8). Computer adaptive testing: Exploring examinee response time using hierarchical linear modeling. Paper presented at the annual meeting of the National Council on Measurement in Education, New Orleans, LA. https://files.eric.ed.gov/fulltext/ED400287.pdf
  • Borgonovi, F., Ferrara, A., & Piacentini, M. (2021). Performance decline in a low-stakes test at age 15 and educational attainment at age 25: Cross-country longitudinal evidence. Journal of Adolescence, 92, 114-125. https://doi.org/10.1016/j.adolescence.2021.08.011
  • Bridgeman, B., & Cline, F. (2000). Variations in mean response time for questions on the computer-adaptive GRE General Test: Implications for fair assessment. GRE Board Professional Report No. 96 20P. Educational Testing Service. https://doi.org/10.1002/j.2333-8504.2000.tb01830.x
  • Chae, Y.M., Park, S.G., & Park, I. (2019). The relationship between classical item characteristics and item response time on computer-based testing. Korean Journal of Medical Education, 31(1), 1-9. https://doi.org/10.3946/kjme.2019.113
  • Chen, G., Cheng, W., Chang, T.W., Zheng, X., & Huang, R. (2014). A comparison of reading comprehension across paper, computer screens, and tablets: Does tablet familiarity matter? Journal of Computers in Education, 1(3), 213 225. http://dx.doi.org/10.1007%2Fs40692-014-0012-z
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  • Cole, J.S., Bergin, D.A., & Whittaker, T.A. (2008). Predicting student achievement for low stakes tests with effort and task value. Contemporary Educational Psychology, 33(4), 609–624. https://doi.org/10.1016/j.cedpsych.2007.10.002
  • Cooper, J. (2006). The digital divide: The special case of gender. Journal of Computer Assisted Learning, 22, 320–334. https://doi.org/10.1111/j.1365-2729.2006.00185.x
  • Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Wadsworth.
  • Çokluk, Ö., Gül, E., & Doğan-Gül, C. (2016). Examining differential item functions of different item ordered test forms according to item difficulty levels. Educational Sciences-Theory & Practice, 16(1), 319-330. https://doi.org/10.12738/estp.2016.1.0329
  • DeMars, C.E. (2007). Changes in rapid-guessing behavior over a series of assessments. Educational Assessment, 12(1), 23–45. https://doi.org/10.1080/10627190709336946
  • Eklöf, H. (2007). Test-taking motivation and mathematics performance in the TIMSS 2003. International Journal of Testing, 7(3), 311 326. https://doi.org/10.1080/15305050701438074
  • Eklöf, H. (2010). Skill and will: Test-taking motivation and assessment quality. Assessment in Education Principles Policy Practice, 17, 345 356. https://doi.org/10.1080/0969594X.2010.516569
  • Fan, Z., Wang, C., Chang, H.-H., & Douglas, J. (2012). Response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37(5), 655-670. http://dx.doi.org/10.3102/1076998611422912
  • Fishbein, B., Foy, P., & Yin, L. (2021). TIMSS 2019 user guide for the international database (2nd ed.). TIMSS & PIRLS International Study Center.
  • Gneezy, U., List, J.A., Livingston, J.A., Qin, X., Sadoff, S., & Xu, Y. (2019). Measuring success in education: the role of effort on the test itself. American Economic Review: Insights, 1(3), 291-308. http://dx.doi.org/10.1257/aeri.20180633
  • Guo, H., Rios, J.A., Haberman, S., Liu, O.L., Wang, J., & Paek, I. (2016). A new procedure for detection of students’ rapid guessing responses using response time. Applied Measurement in Education, 29(3), 173 183. https://doi.org/10.1080/08957347.2016.1171766
  • Hannula. (2004). Development of understanding and self-confidence in mathematics, grades 5-8. Proceding of the 28th Conference of the International Group for the Psychology of Mathematics Education, 3, 17-24. http://files.eric.ed.gov/fulltext/ED489565.pdf
  • Hess, B.J., Johnston, M.M., & Lipner, R.S. (2013). The impact of item format and examinee characteristics on response times. International Journal of Testing, 13(4), 295–313. https://doi.org/10.1080/15305058.2012.760098
  • Hoffman, B. (2010). “I think I can, but I'm afraid to try”: The role of self-efficacy beliefs and mathematics anxiety in mathematics problem-solving efficiency. Learning and Individual Differences, 20(3), 276-283. https://doi.org/10.1016/j.lindif.2010.02.001
  • Hoffman, B., & Spatariu, A. (2008). The influence of self-efficacy and metacognitive prompting on math problem-solving efficiency. Contemporary Educational Psychology, 33(4), 875-893. https://doi.org/10.1016/j.cedpsych.2007.07.002
  • İlgün-Dibek, M. (2020). Silent predictors of test disengagement in PIAAC 2012. Journal of Measurement and Evaluation in Education and Psychology, 11(4), 430-450. https://doi.org/10.21031/epod.796626
  • İlhan, M., Öztürk, N.B., & Şahin, M.G. (2020). The effect of the item’s type and cognitive level on its difficulty index: The sample of the TIMSS 2015. Participatory Educational Research, 7(2), 47-59. https://doi.org/10.17275/per.20.19.7.2
  • Koçdar, S., Karadağ, N., & Şahin, M.D. (2016). Analysis of the difficulty and discrimination indices of multiple-choice questions according to cognitive levels in an open and distance learning context. The Turkish Online Journal of Educational Technology, 15(4), 16–24. https://hdl.handle.net/11421/11442
  • Lasry, N., Watkins, J., Mazur, E., & Ibrahim, A. (2013). Response times to conceptual questions. American Journal of Physics, 81(9), 703 706. https://doi.org/10.1119/1.4812583
  • Lee, Y.H., & Chen, H. (2011). A review of recent response-time analyses in educational testing. Psychological Test and Assessment Modeling, 53(3), 359–379.
  • 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(8), 1-24. https://doi.org/10.1186/s40536-014-0008-1
  • Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: A causal analysis. Computers in Human Behavior, 14(1), 125 146. http://dx.doi.org/10.1016/0747-5632(93)90033-O
  • Lundgren, E., & Eklöf, H. (2020). Within-item response processes as indicators of test-taking effort and motivation. Educational Research and Evaluation, 26(5-6), 275-301. https://doi.org/10.1080/13803611.2021.1963940
  • Martin, M.O., von Davier, M., & Mullis, I.V.S. (Eds.). (2020). Methods and procedures: The TIMSS 2019 technical report. The TIMSS & PIRLS International Study Center. https://www.iea.nl/publications/technical-reports/methods-and-procedures-timss-2019-technical-report
  • Michaelides, M.P., Ivanova, M., & Nicolaou, C. (2020). The relationship between response-time effort and accuracy in PISA science multiple choice items. International Journal of Testing, 20(3), 187-205. https://doi.org/10.1080/15305058.2019.1706529
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There are 69 citations in total.

Details

Primary Language English
Subjects Psychological Methodology, Design and Analysis
Journal Section Special Issue 2023
Authors

Esin Yılmaz Koğar 0000-0001-6755-9018

Sümeyra Soysal 0000-0002-7304-1722

Publication Date December 27, 2023
Submission Date August 15, 2023
Published in Issue Year 2023

Cite

APA Yılmaz Koğar, E., & Soysal, S. (2023). Examination of response time effort in TIMSS 2019: Comparison of Singapore and Türkiye. International Journal of Assessment Tools in Education, 10(Special Issue), 174-193. https://doi.org/10.21449/ijate.1343248

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