Skip to main content

Advertisement

Log in

Technology effectiveness in the mathematics classroom: a systematic review of meta-analytic research

  • Published:
Journal of Computers in Education Aims and scope Submit manuscript

Abstract

The purpose of this systematic review was to examine trends in prior meta-analytic research to provide recommendations for future mathematics education research and instructional praxis. The current study aims to contextualize the effects of technology-enhanced instruction in the mathematics classroom. The researchers conducted a comprehensive literature search of articles written between 1980 and 2015. The final pool of studies comprised 18 meta-analyses inclusive of studies conducted between 1986 and 2014, representing 1193 independent effect sizes. The results suggest that the effects of technology on mathematics achievement range from small to large. Results suggest that researchers and educators should consider grade level, duration, and the instructional role of technology as key components when incorporating technology in the mathematics classroom. Results also suggest that race, socioeconomic status (SES), and gender did not moderate the effects of technology integration, although they were examined less frequently across studies. Implications are provided for practice, and research related to these results. Because of the chosen research approach, the research results provide relevant and practical implications to support classroom teaching with technology. This study contributes to the literature on technology-enhanced mathematics instruction by providing synthesis of 30 years of meta-analytic research.

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

Similar content being viewed by others

References

  • Aguinis, H., Gottfredson, R. K., & Wright, T. A. (2011). Best-practice recommendations for estimating interaction effects using meta-analysis. Journal of Organizational Behavior, 32(8), 1033–1043.

    Article  Google Scholar 

  • American Educational Research Association. (2006). Standards for reporting on empirical social science research in AERA publications. Educational Researcher, 35(6), 33–40.

    Article  Google Scholar 

  • American Psychological Association. (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: American Psychological Association.

    Google Scholar 

  • Bano, M., Zowghi, D., Kearney, M., Schuck, S., & Aubusson, P. (2018). Mobile learning for science and mathematics school education: A systematic review of empirical evidence. Computers & Education, 121, 30–58.

    Article  Google Scholar 

  • Brown, E. T., Karp, K., Petrosko, J. M., Jones, J., Beswick, G., Howe, C., et al. (2007). Crutch or catalyst: Teachers’ beliefs and practices regarding calculator use in mathematics instruction. School Science and Mathematics, 107(3), 102–116.

    Article  Google Scholar 

  • Chadwick, D. K. H. (1997). Computer-assisted instruction in secondary mathematics classrooms: A meta-analysis (Ed.D.). Drake University, United States-Iowa. Retrieved from http://search.proquest.com/pqdtglobal/docview/304389928/abstract/9D8837D0D8EB4A14PQ/11. Accessed 10 Sept 2015.

  • Chan, K. K., & Leung, S. W. (2014). Dynamic geometry software improves mathematical achievement: Systematic review and meta-Analysis. Journal of Educational Computing Research, 51(3), 311–325.

    Article  Google Scholar 

  • Chang, C. Y., Lai, C. L., & Hwang, G. J. (2018). Trends and research issues of mobile learning studies in nursing education: A review of academic publications from 1971 to 2016. Computers & Education, 116, 28–48.

    Article  Google Scholar 

  • Chen, T. Y. (1994). A meta-analysis of effectiveness of computer-based instruction in mathematics (Ph.D.). The University of Oklahoma, United States - Oklahoma. Retrieved from http://search.proquest.com/pqdtglobal/docview/304105627/abstract/9D8837D0D8EB4A14PQ/5. Accessed 10 Sept 2015.

  • Cheung, A. C. K., & Slavin, R. E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88–113.

    Article  Google Scholar 

  • Close, S., Oldham, E., Shiel, G., Dooley, T., & O’Leary, M. (2012). Effects of calculators on mathematics achievement and attitudes of ninth-grade students. Journal of Educational Research, 105(6), 377–390.

    Article  Google Scholar 

  • Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.

    Article  Google Scholar 

  • Cooper, H. (2016). Research synthesis and meta-analysis: A step-by-step approach (Vol. 2). Thousand Oaks: Sage publications.

    Google Scholar 

  • Cooper, H., & Patall, E. A. (2009). The relative benefits of meta-analysis conducted with individual participant data versus aggregated data. Psychological Methods, 14(2), 165–176.

    Article  Google Scholar 

  • Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-analysis. Routledge: Routledge publications.

  • Davis, M. R. (2010). Solving algebra on smartphones. Technology Counts, 29(26), 20–23.

    Google Scholar 

  • DeCoster, J. (2004). Meta-analysis notes. Retrieved from http://www.stathelp.com/Meta%20analysis%202009-06-01.pdf. Accessed 7 Nov 2015.

  • Ellington, A. J. (2006). The effects of non-CAS graphing calculators on student achievement and attitude levels in mathematics: A meta-analysis. School Science and Mathematics, 106(1), 16–23.

    Article  Google Scholar 

  • Fabian, K., Topping, K. J., & Barron, I. G. (2016). Mobile technology and mathematics: Effects on students’ attitudes, engagement, and achievement. Journal of Computers in Education, 3(1), 77–104.

    Article  Google Scholar 

  • Fu, Q. K., & Hwang, G. J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers & Education, 119, 129–143.

    Article  Google Scholar 

  • Gay, A. S., & Burbridge, L. (2016). “Bring Your Own Device” for Formative Assessment. Mathematics Teacher, 110(4), 310–313.

    Article  Google Scholar 

  • Gurevitch, J.,Koricheva, J.,Nakagawa, S., & Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature, 555 (7695), Retrieved from https://www.nature.com/articles/nature25753.pdf. Accessed 15 Mar 2018.

  • Hembree, R., & Dessart, D. J. (1986). Effects of hand-held calculators in precollege mathematics education: A meta-analysis. Journal for Research in Mathematics Education, 17, 83–99.

    Article  Google Scholar 

  • Hicks, D., & Holden, C. (2007). Teaching the global dimension: Key principles and effective practice. London: Routledge.

    Google Scholar 

  • Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2(3), 172–177.

    Article  Google Scholar 

  • Hsu, Y. (2003). The effectiveness of computer-assisted instruction in statistics education: A meta-analysis (Ph.D.). The University of Arizona, United States - Arizona. Retrieved from http://search.proquest.com/pqdtglobal/docview/305338759/abstract/9D8837D0D8EB4A14PQ/29. Accessed 1 Aug 2015.

  • Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.). Thousand Oaks: Sage.

    Book  Google Scholar 

  • King H. J. (1997). Effects of computer-enhanced instruction in college-level mathematics as determined by a meta-analysis (vol. 59, p. 114A). United States: The University of Tennessee.

  • Kulik, J. A. (1983). Synthesis of research on computer-based instruction. Educational Leadership, 41(1), 19–21.

    Google Scholar 

  • Larwin, K., & Larwin, D. (2011). A meta-Analysis examining the impact of computer-assisted instruction on postsecondary statistics education: 40 years of research. Journal of Research on Technology in Education, 43(3), 253–278.

    Article  Google Scholar 

  • Lee, W. C. (1990). The effectiveness of computer-assisted instruction and computer programming in elementary and secondary mathematics: A meta-analysis (Ed.D.). University of Massachusetts Amherst, United States - Massachusetts. Retrieved from http://search.proquest.com/pqdtglobal/docview/303852079/abstract/9D8837D0D8EB4A14PQ/18.

  • Li, Q., & Ma, X. (2010). A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review, 22(3), 215–243.

    Article  Google Scholar 

  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for sys- tematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151, 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135.

    Article  Google Scholar 

  • Moyer-Packenham, P. S., & Westenskow, A. (2013). Effects of virtual manipulatives on student achievement and mathematics learning. International Journal of Virtual and Personal Learning Environments, 4(3), 35–50.

  • National Council of Teachers of Mathematics. (2000). Principles and standards of school mathematics. Reston: Author.

    Google Scholar 

  • Nepo, K. (2017). The use of technology to improve education. Child & Youth Care Forum, 46(2), 207–221.

    Article  Google Scholar 

  • Nikolaou, C. (2001). Hand-held calculator use and achievement in mathematics: A meta analysis (Ph.D.). Georgia State University, United States - Georgia. Retrieved from http://search.proquest.com/pqdtglobal/docview/304696658/abstract/9D8837D0D8EB4A14PQ3. Accessed 10 Sept 2015.

  • Planty, M., Hussar, W., Snyder, T., Kena, G., KewalRamani, A., Kemp, J., Bianco, K., & Dinkes, R. (2009). The Condition of Education 2009 (NCES 2009-081). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC.

  • Rosen, Y., & Salomon, G. (2007). The differential learning achievements of constructivist technology-intensive learning environments as compared with traditional ones: A meta-analysis. Journal of Educational Computing Research, 36(1), 1–14.

    Article  Google Scholar 

  • Rosenthal, R. (1991). Meta-analytic procedures for social research (rev ed.). Beverly Hills: Sage.

    Book  Google Scholar 

  • Schenker, J. D. (2007). The effectiveness of technology use in statistics instruction in higher education: A meta-analysis using hierarchical linear modeling. (Doctoral dissertation). Retrieved from ProQuest Digital Dissertations. (AAT 3286857).

  • Schmidt, F. L., & Hunter, J. E. (2014). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks: Sage publications.

    Google Scholar 

  • Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning. Journal of Educational Psychology, 105(4), 970–987.

  • Steel, P. D., & Kammeyer-Mueller, J. D. (2002). Comparing meta-analytic moderator estimation techniques under realistic conditions. Journal of Applied Psychology, 87(1), 96–111.

    Article  Google Scholar 

  • Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational research, 81(1), 4–28.

    Article  Google Scholar 

  • Tokpah, C. L. (2008). The effects of computer algebra systems on students’ achievement in mathematics (Ph.D.). Kent State University, United States - Ohio. Retrieved from http://search.proquest.com/pqdtglobal/docview/304549974/abstract/9D8837D0D8EB4A14PQ. Accessed 1 Dec 2015.

  • Valk, J., Rashid, A. T., & Elder, L. (2010). Using mobile phones to improve educational outcomes: An analysis of evidence from Asia. International Review of Research in Open and Distance Learning, 11(1), 117–140.

    Google Scholar 

  • Wang, H. Y., Liu, G. Z., & Hwang, G. J. (2017). Integrating socio-cultural contexts and location-based systems for ubiquitous language learning in museums: A state of the art review of 2009–2014. British Journal of Educational Technology, 48(2), 653–671.

    Article  Google Scholar 

  • Wang, S., Jiao, H., Young, M. J., Brooks, T., Olson, J. (2007). A meta-analysis of testing mode effects in grade K-12 mathematics tests. Educational and Psychological Measurement, 67(2), 219–238

    Article  Google Scholar 

  • Young, J. (2017). Technology-enhanced mathematics instruction: A second-order meta-analysis of 30 years of research. Educational Research Review, 22, 19–33

    Article  Google Scholar 

  • Young, J. L., Young, J. R., & Capraro, R. M. (2018). Gazing past the gaps: A growth-based assessment of the mathematics achievement of black girls. The Urban Review, 50(1), 156–176.

    Article  Google Scholar 

  • Young, J. R., & Young, J. L. (2012). “But that’s not fair”: Teacher technology readiness and African American Students’. The Journal of the Texas Alliance of Black School Educators, 4(1), 19–32.

    Google Scholar 

  • Young, J. R., Young, J. L., & Hamilton, C. (2013). The use of confidence intervals as a meta-analytic lens to summarize the effects of teacher education technology courses on preservice teacher TPACK. Journal of Research on Technology in Education, 46(2), 149–172.

    Article  Google Scholar 

  • Young, J. R., & Young, J. L. (2016). Young, black, and anxious: Describing the black student mathematics anxiety research using confidence intervals. Journal of Urban Mathematics Education, 9(1), 79–93.

    Google Scholar 

  • Yung, H. I., & Paas, F. (2015). Effects of computer-based visual representation on mathematics learning and cognitive load. Educational Technology and Society, 18(4), 70–77.

    Google Scholar 

  • Zientek, L. R., Capraro, M. M., & Capraro, R. M. (2008). Reporting practices in quantitative teacher education research: One look at the evidence cited in the AERA Panel Report. Educational Researcher, 37(4), 208–216.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jamaal Young.

Ethics declarations

Conflict of interest

All authors declared that they have no conflict of interest

Ethical approval

No animals were involved in this project. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Young, J., Gorumek, F. & Hamilton, C. Technology effectiveness in the mathematics classroom: a systematic review of meta-analytic research. J. Comput. Educ. 5, 133–148 (2018). https://doi.org/10.1007/s40692-018-0104-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40692-018-0104-2

Keywords

Navigation