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.
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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.
Battista, M. T. (1990). Spatial visualization and gender differences in high school geometry. Journal for Research in Mathematics Education, 21, 47–60.
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.
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.
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.
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.
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.
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.
DeAngelis, S. (2000). Equivalency of computer-based and paper-and-pencil testing. Journal of Allied Health, 29(3), 161–164.
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.
Ekstrom, R. B., French, J. W., Harman, H., & Derman, D. (1976). Kit of factor-referenced cognitive tests. Princeton: Educational Testing Service.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Thousand Oaks, CA: Sage Publications Inc.
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.
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.
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.
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.
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.
Lowrie, T., & Diezmann, C. M. (2007). Solving graphics problems: Student performance in the junior grades. Journal of Educational Research, 100(6), 369–377.
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.
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.
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.
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.
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.
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.
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.
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.
Skemp, R. R. (1986). The psychology of learning mathematics. London: Penguin.
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312.
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.
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.
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.
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.
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.
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.
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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.
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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
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DOI: https://doi.org/10.1007/s13394-015-0143-1