Skip to main content
Log in

The influence of test mode and visuospatial ability on mathematics assessment performance

  • Original Article
  • Published:
Mathematics Education Research Journal Aims and scope Submit manuscript

Abstract

Mathematics assessment and testing are increasingly situated within digital environments with international tests moving to computer-based testing in the near future. This paper reports on a secondary data analysis which explored the influence the mode of assessment—computer-based (CBT) and pencil-and-paper based (PPT)—and visuospatial ability had on students’ mathematics test performance. Data from 804 grade 6 Singaporean students were analysed using the knowledge discovery in data design. The results revealed statistically significant differences between performance on CBT and PPT test modes across content areas concerning whole number algebraic patterns and data and chance. However, there were no performance differences for content areas related to spatial arrangements geometric measurement or other number. There were also statistically significant differences in performance between those students who possess higher levels of visuospatial ability compared to those with lower levels across all six content areas. Implications include careful consideration for the comparability of CBT and PPT testing and the need for increased attention to the role of visuospatial reasoning in student’s mathematics reasoning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Baker, R. S. J. D. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education (3rd ed., pp. 112–118). Oxford, UK: Elsevier.

    Chapter  Google Scholar 

  • Battista, M. T. (1990). Spatial visualization and gender differences in high school geometry. Journal for Research in Mathematics Education, 21, 47–60.

    Article  Google Scholar 

  • Bennett, R. E., Braswell, J., Oranje, A., Sandene, B., Kaplan, B., & 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), Retrieved September 25 from http://www.jtla.org

  • Bugbee, A. C. (1996). The equivalence of paper-and-pencil and computer-based testing. Journal of Research on Computing in Education, 28(3), 282–290.

    Article  Google Scholar 

  • Casey, M. B., Nuttall, R. L., & Pezaris, E. (1997). Mediators of gender differences in mathematics college entrance test scores: A comparison of spatial skills with internalized beliefs and anxieties. Developmental Psychology, 33, 669–680.

    Article  Google Scholar 

  • Cheng, Y.-L., & Mix, K. S. (2014). Spatial training improves children’s mathematics ability. Journal of Cognition and Development, 15(1), 2–11. doi:10.1080/15248372.2012.725186.

    Article  Google Scholar 

  • Clariana, R., & Wallace, P. (2002). Paper-based versus computer-based assessment: key factors associated with test mode effect. British Journal of Educational Technology, 33(5), 593–602.

    Article  Google Scholar 

  • Clements, D. H. (2004). Geometric and spatial thinking in early childhood education. In D. H. Clements, J. Sarama, A.-M. DiBiase, & A.-M. DiBiase (Eds.), Engaging young children in mathematics: standards for early childhood mathematics education (pp. pp. 267–pp. 297). Mahwah: Erlbaum.

    Google Scholar 

  • Clements, D. H., & Battista, M. T. (1992). Geometry and spatial reasoning. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 420–464). New York: Macmillan.

    Google Scholar 

  • DeAngelis, S. (2000). Equivalency of computer-based and paper-and-pencil testing. Journal of Allied Health, 29(3), 161–164.

    Google Scholar 

  • Devine, P. (2003). Secondary data analysis. In R. L. Miller & J. D. Brewer (Eds.), The A-Z of social research (pp. pp. 286–pp. 289). London: SAGE Publications, Ltd. doi:10.4135/9780857020024.n97.

    Google Scholar 

  • Ekstrom, R. B., French, J. W., Harman, H., & Derman, D. (1976). Kit of factor-referenced cognitive tests. Princeton: Educational Testing Service.

    Google Scholar 

  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.

    Google Scholar 

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Thousand Oaks, CA: Sage Publications Inc.

    Google Scholar 

  • Fuchs, L. S., Geary, D. C., Compton, D. L., Fuchs, D., Hamlett, C. L., Seethaler, P. M., Bryant, J. V., & Schatschneider, C. (2010). Do different types of school mathematics development depend on different constellations of numerical and general cognitive abilities? Developmental Psychology, 46, 1731–1746. doi:10.1037/a0020662.

    Article  Google Scholar 

  • Halpern, D. F., & Collaer, M. L. (2005). Sex differences in visuospatial ability: more than meets the eye. In P. Shah & A. Miyake (Eds.), The Cambridge Handbook of Visuospatial Thinking (pp. 170–212). New York: Cambridge University Press.

    Chapter  Google Scholar 

  • Hardré, P. L., Crowson, H. M., Xie, K., & Ly, C. (2007). Testing differential effects of computer-based, web-based and paper-based administration of questionnaire research instruments. British Journal of Educational Technology, 38(1), 5–22.

    Article  Google Scholar 

  • Johnson, M., & Green, S. (2006). On-Line mathematics assessment: The impact of mode on performance and question answering strategies. Journal of Technology, Learning, and Assessment, 4(5). Retrieved September 25 from http://www.jtla.org.

  • Kirby, J. R., & Boulter, D. R. (1999). Spatial ability and transformational geometry. European Journal of Psychology of Education, 14(2), 283–294.

    Article  Google Scholar 

  • Kozhevnikov, M., Hegarty, M., & Mayer, R. E. (1999). Students’ use of imagery in solving qualitative problems in kinematics. Washington, DC: U.S Department of Education. (ERIC Document Reproduction Service No. ED433239).

  • Kyttälä, M. (2008). Visuospatial working memory in adolescents with poor performance in mathematics: Variation depending on reading skills. Educational Psychology: An International Journal of Experimental Educational Psychology, 28(3), 273–289.

    Article  Google Scholar 

  • Lowrie, T., & Diezmann, C. M. (2007). Solving graphics problems: Student performance in the junior grades. Journal of Educational Research, 100(6), 369–377.

    Article  Google Scholar 

  • MacDonald, A. S. (2002). The impact of individual differences on the equivalence of computer-based and paper-and-pencil educational assessments. Computers and Education, 39, 299–312.

    Article  Google Scholar 

  • Marginson, S., Tytler, R., Freeman, B., & Roberts, K. (2013). STEM: Country comparisons. Report for the Australian Council of Learned Academies. Melbourne, Vic: Australian Council of Learned Academies. Retrieved 17 Feb 2015 from www.acola.org.au.

  • Mayer, R. E., & Massa, L. J. (2003). Three facets of visual and verbal learners: cognitive ability, cognitive style, and learning preference. Journal of Educational Psychology, 95(4), 833–846.

    Article  Google Scholar 

  • Mullis, I. V. S., Martin, M. O., Ruddock, G. J., O’Sullivan, C. Y., & Preuschoff, C. (2009). Chestnut Hill, MA: TIMSS & PIRLS International Study Center. International Association for the: Evaluation of Educational Achievement (IEA). TIMSS 2011 Assessment Frameworks.

    Google Scholar 

  • Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 International results in mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, International Association for the Evaluation of Educational Achievement (IEA).

  • Ng, S. F., & Lee, K. (2009). The model method: Singapore children’s tool for representing and solving algebraic word problems. Journal for Research in Mathematics Education, 40(3), 282–313.

    Google Scholar 

  • Pittalis, M., & Christou, C. (2010). Types of reasoning in 3D geometry thinking and their relation with spatial ability. Educational Studies in Mathematics, 75, 191–212. doi:10.1007/s10649-010-9251-8.

    Article  Google Scholar 

  • Reuhkala, M. (2001). Mathematical skills in ninth-graders: relationship with visuo-spatial abilities and working memory. Educational Psychology: An International Journal of Experimental Educational Psychology, 21(4), 387–399. doi:10.1080/01443410120090786.

    Article  Google Scholar 

  • Rohde, T. E., & Thompson, L. A. (2007). Predicting academic achievement with cognitive ability. Intelligence, 35, 83–92. doi:10.1016/j.intell.2006.05.004.

    Article  Google Scholar 

  • Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.

    Article  Google Scholar 

  • Skemp, R. R. (1986). The psychology of learning mathematics. London: Penguin.

    Google Scholar 

  • Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312.

    Article  Google Scholar 

  • Threlfall, J., Pool, P., Homer, M., & Swinnerton, B. (2007). Implicit aspects of paper and pencil mathematics assessment that come to light through the use of the computer. Educational Studies in Mathematics, 66, 335–348. doi:10.1007/s10649-006-9078-5.

    Article  Google Scholar 

  • Tolar, T. D., Lederberg, A. R., & Fletcher, J. M. (2009). A structural model of algebra achievement: computational fluency and spatial visualisation as mediators of the effect of working memory on algebra achievement. Educational Psychology: An International Journal of Experimental Educational Psychology, 29(2), 239–266. doi:10.1080/01443410802708903.

    Article  Google Scholar 

  • Tversky, B. (2004). Visuospatial reasoning. In K. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 209–240). New York: Cambridge University Press.

    Google Scholar 

  • Uttal, D. H., & Cohen, C. A. (2012). Spatial thinking in STEM education: when, why and how. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 57, pp. 147–182). San Diego: Academic.

    Chapter  Google Scholar 

  • Wang, S., Jiao, H., Young, M. J., Brooks, T., & Olson, J. (2008). Comparability of computer-based and paper-and-pencil testing in k–12 reading assessments: A meta-analysis of testing mode effects. Educational and Psychological Measurement, 68(1), 5–24.

    Google Scholar 

  • Zhang, X., Koponen, T., Räsänen, P., Aunola, K., Lerkkanen, M.-K., & Nurmi, J.-E. (2014). Linguistic and spatial skills predict early arithmetic development via counting sequence knowledge. Child Development, 85(3), 1091–1107. doi:10.1111/cdev.12173.

    Article  Google Scholar 

Download references

Acknowledgments

The author wishes to thank Professor Tom Lowrie for generously allowing me to re-analyse his data and for his comments and insights into the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tracy Logan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Logan, T. The influence of test mode and visuospatial ability on mathematics assessment performance. Math Ed Res J 27, 423–441 (2015). https://doi.org/10.1007/s13394-015-0143-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13394-015-0143-1

Keywords

Navigation