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Latent Growth Modeling of Item Process Data Derived From Eye-tracking Technology: An Experimental Study Investigating Reading Behavior of Examinees When Answering A Multiple-Choice Test Item

Year 2022, Volume: 13 Issue: 3, 194 - 211, 30.09.2022
https://doi.org/10.21031/epod.1107597

Abstract

This study illustrates how eye-tracking data can be translated to “item process data” for multiple-choice test items to study the relationship between subjects’ item responses and choice reading behavior. Several modes of analysis were used to test the hypothesized added value of using process data to identify choice reading patterns of subjects. In addition to the cross-sectional analises of agggregate measurements derived from the time series eye tracking data, Latent Growth Curve Model Analises were undertaken to test if the the shape of change observed in the sequential choice reading patterns differed for subjects depending on their responses to the item being correct or incorrect. Application data were from an experimental study and included seventy-one subjects’ responses to two multiple-choice test items measuring reading comprehension ability in English as a second language. Analyses were carried out for one item at a time. For each item, first, each subject’s recorded eye movements were coded into a set of Area of Interests (AOIs), segmenting the lines in the stem and the individual choices. Next, each subject’s fixation times on the AOIs were time stamped into seconds, indicating when and in what order each subject’s gaze had fixated on each AOI until a choice was marked as the correct answer, which ended the item encounter. A set of nested Latent Growth Curve models were considered for the choice-related AOIs to deliniate if distinct choice-process sequences were evident for correct and incorrect respoders. Model fit indices, random intercepts, slopes, and residuals were computed using the mean log fixation times over item encounter time. The results show that the LGM with the best model fit indicies, for both items, was the quadratic model using response variable as a covariate. Albeit limited due to the two-item – seventy-one subjects experimental setting of the study, the findings are promising and show that utilizing item-level process data can be very useful for defining distinct choice processing (task-oriented reading) patterns of examinees. Over all, the results warrant further study of choice derived AOIs using longitudinal statistical models. It is argued that, the screening methodology desribed in this study can be a useful tool to investigate speededness, distractor functioning, or even to flag subjects with irregular choice processing behavior, such as providing a direct mark on a choice, without any significant reading activity on any of the choices presented (i.e., whether cheating might have occurred.)

References

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  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (2014). Standards for educational and psychological testing. American Educational Research Association.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
  • Bentler, P. M. (1995). EQS structural equations program manual (vol. 6). Multivariate software. https://doi.org/10.4236/am.2014.510132
  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
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  • Paulson, E. J., & Henry, J. (2002). Does the Degrees of Reading Power assessment reflect the reading process? An eye-movement examination. Journal of Adolescent & Adult Literacy, 46(3), 234-244. https://link.gale.com/apps/doc/A94123361/LitRC?u=anon~924799ab&sid=bookmark- LitRC&xid=2cf242f9
  • Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E. (2008). Latent growth curve modeling (No. 157). Sage.
  • Raney, G. E., Campbell, S. J., & Bovee, J. C. (2014). Using eye movements to evaluate the cognitive processes involved in text comprehension. JoVE (Journal of Visualized Experiments), 83, e50780. https://doi.org/10.3791/50780
  • Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422. https://doi.org/10.1037/0033-2909.124.3.372
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136
  • Solheim, O. J., & Uppstad, P. H. (2011). Eye-tracking as a tool in process-oriented reading test validation. International Electronic Journal of Elemantary Education, 4(1), 153-168. https://www.iejee.com/index.php/IEJEE/article/view/218
  • Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173-180. https://doi.org/10.1207/s15327906mbr2502_4
  • Tai, R. H., Loehr, J. F., & Brigham, F. J. (2006). An exploration of the use of eye‐gaze tracking to study problem‐ solving on standardized science assessments. International Journal of Research & Method in Education, 29(2), 185-208. https://doi.org/10.1080/17437270600891614
  • Tsai, M. J., Hou, H. T., Lai, M. L., Liu, W. Y., & Yang, F. Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58(1), 375-385. https://doi.org/10.1016/j.compedu.2011.07.012
  • Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change. Psychological Bulletin, 110, 268-290. https://doi.org/10.1037/0033-2909.116.2.363
  • Yaneva, V., Clauser, B. E., Morales, A., & Paniagua, M. (2022). Assessing the validity of test scores using response process data from an eye-tracking study: A new approach. Advances in Health Sciences Education, Online First. https://doi.org/10.1007/s10459-022-10107-9
Year 2022, Volume: 13 Issue: 3, 194 - 211, 30.09.2022
https://doi.org/10.21031/epod.1107597

Abstract

References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (2014). Standards for educational and psychological testing. American Educational Research Association.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
  • Bentler, P. M. (1995). EQS structural equations program manual (vol. 6). Multivariate software. https://doi.org/10.4236/am.2014.510132
  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136−162). Sage.
  • Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2013). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Routledge.
  • Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. OUP Oxford.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Kane, M., & Mislevy, R. (2017). Validating score interpretations based on response processes. In Validation of score meaning for the next generation of assessments (pp. 11-24). Routledge.
  • Mézière, D. C., Yu, L., Reichle, E., von der Malsburg, T., & McArthur, G. (2021). Using eye-tracking measures to predict reading comprehension. https://doi.org/10.31234/osf.io/v2rdp
  • Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class–latent growth modeling. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 291–322). American Psychological Association. https://doi.org/10.1037/10409-010
  • Öğrenci Seçme ve Yerleştirme Merkezi (2018). Retrieved on February 25, from https://www.osym.gov.tr/TR,15313/2018-yds-sonbahar-donemi-temel-soru-kitapciklarinin-yayimlanmasi--10.html
  • Paulson, E. J., & Henry, J. (2002). Does the Degrees of Reading Power assessment reflect the reading process? An eye-movement examination. Journal of Adolescent & Adult Literacy, 46(3), 234-244. https://link.gale.com/apps/doc/A94123361/LitRC?u=anon~924799ab&sid=bookmark- LitRC&xid=2cf242f9
  • Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E. (2008). Latent growth curve modeling (No. 157). Sage.
  • Raney, G. E., Campbell, S. J., & Bovee, J. C. (2014). Using eye movements to evaluate the cognitive processes involved in text comprehension. JoVE (Journal of Visualized Experiments), 83, e50780. https://doi.org/10.3791/50780
  • Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372-422. https://doi.org/10.1037/0033-2909.124.3.372
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136
  • Solheim, O. J., & Uppstad, P. H. (2011). Eye-tracking as a tool in process-oriented reading test validation. International Electronic Journal of Elemantary Education, 4(1), 153-168. https://www.iejee.com/index.php/IEJEE/article/view/218
  • Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25(2), 173-180. https://doi.org/10.1207/s15327906mbr2502_4
  • Tai, R. H., Loehr, J. F., & Brigham, F. J. (2006). An exploration of the use of eye‐gaze tracking to study problem‐ solving on standardized science assessments. International Journal of Research & Method in Education, 29(2), 185-208. https://doi.org/10.1080/17437270600891614
  • Tsai, M. J., Hou, H. T., Lai, M. L., Liu, W. Y., & Yang, F. Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58(1), 375-385. https://doi.org/10.1016/j.compedu.2011.07.012
  • Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change. Psychological Bulletin, 110, 268-290. https://doi.org/10.1037/0033-2909.116.2.363
  • Yaneva, V., Clauser, B. E., Morales, A., & Paniagua, M. (2022). Assessing the validity of test scores using response process data from an eye-tracking study: A new approach. Advances in Health Sciences Education, Online First. https://doi.org/10.1007/s10459-022-10107-9
There are 24 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ergün Cihat Çorbacı 0000-0002-7874-956X

Nilüfer Kahraman 0000-0003-2523-0155

Publication Date September 30, 2022
Acceptance Date August 25, 2022
Published in Issue Year 2022 Volume: 13 Issue: 3

Cite

APA Çorbacı, E. C., & Kahraman, N. (2022). Latent Growth Modeling of Item Process Data Derived From Eye-tracking Technology: An Experimental Study Investigating Reading Behavior of Examinees When Answering A Multiple-Choice Test Item. Journal of Measurement and Evaluation in Education and Psychology, 13(3), 194-211. https://doi.org/10.21031/epod.1107597