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Students’ use of electronic support tools in mathematics

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Abstract

This study investigated students’ use of electronic support tools within a computer-based mathematics program. Electronic support tools are tools, such as hyperlinks or calculators, available within many computer-based instructional programs. A convenience sample of 73 students in grades 4–6 was selected to participate in the study. Students completed online lessons over the course of 6 weeks. Lessons were chosen to supplement the core instruction students’ received in their mathematics classes. Correlations were found between students’ use of specific electronic support tools and their basic academic skill fluency. A significant difference was also found between the pre and post mathematics test [t(72) = 6.463, p < .001, Cohen’s d = 1.52]. Structural equation modeling was used to examine the direct and indirect effects of prior academic skills (a latent variable comprised of working memory, math fact fluency, and reading rate) and the overall use of electronic support tools on gains between the pre and posttest. Results indicated that having stronger prior academic skills contributed to significantly lower levels of EST use for students, and having weaker prior academic skills contributed to significantly higher levels of EST use. EST use, in turn, positively predicted gains from the program, indicating an indirect effect of prior academic skills on gain scores.

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References

  • Adams, J. W., & Hitch, G. J. (1997). Working memory and children’s mental addition. Journal of Experimental Child Psychology, 67, 21–28.

    Article  Google Scholar 

  • Aleks©. (2013). Retrieved from http://www.aleks.com/about_aleks/overview.

  • Aleven, V., & Koedinger, K. R. (2000). Limitations of student control: Do students know when they need help? In G. Gauthier, C. Frasson, & K. VanLehn (Eds.), Proceedings of the 5th international conference on intelligent tutoring systems, ITS 2000 (pp. 292–303). Berlin: Springer.

    Google Scholar 

  • Aleven, V., McLaren, B. M., Roll, I., & Koedinger, K. R. (2006). Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16, 101–128.

    Google Scholar 

  • Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2010). Automated, unobtrusive, action-by-action assessment of self-regulation during learning with an intelligent tutoring system. Educational Psychologist, 45(4), 224–233. doi:10.1080/00461520.2010.517740.

    Article  Google Scholar 

  • Aleven, V., Stahl, E., Schworm, S., Fishcer, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73(3), 277–320.

    Article  Google Scholar 

  • Alsop, G., & Tompsett, C. (2007). From effect to effectiveness: The missing research questions. Educational Technology & Society, 10(1), 28–39.

    Google Scholar 

  • Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.

    Article  Google Scholar 

  • Anderson, U., & Lyxell, B. (2007). Working memory deficit in children with mathematical difficulties: A general or specific deficit. Journal of Experimental Child Psychology, 96, 197–228. doi:10.1016/j.jecp.2006.10.001.

    Article  Google Scholar 

  • Anderson-Inman, L., & Horney, M. (1996). Computer-based concept mapping: Enhancing literacy tools for visual thinking. Journal of Adolescent & Adult Literacy, 40, 302–306.

    Google Scholar 

  • Arbreton, A. (1998). Student goal orientation and help-seeking strategy use. In S. A. Karabenick (Ed.), Strategic help seeking: Implications for learning and teaching (pp. 95–116). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Baddeley, A. (1992). Working memory. Science, 255, 556–559.

    Article  Google Scholar 

  • Baddeley, A. (1996). Exploring the central executive. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 49, 5–28. doi:10.1080/027249896392784.

    Article  Google Scholar 

  • Blok, H., Oostdam, R., Otter, M. E., & Overmaat, M. (2002). Computer-assisted instruction in support of beginning reading instruction: A review. Review of Educational Research, 72(1), 101–130. doi:10.3102/00246543072001101.

    Article  Google Scholar 

  • Brunvand, S., & Abadeh, H. (2010). Making online learning accessible: Using technology to declutter the web. Intervention in School and Clinic, 45, 304–312.

    Article  Google Scholar 

  • Carr, M., & Hettinger, H. (2003). Perspectives on mathematics strategy development. In J. M. Royer (Ed.), Mathematical cognition (pp. 33–68). Greenwich, CT: Information Age Publishing.

    Google Scholar 

  • Cemal Nat, M., Walker, S., Bacon, L., Dastbaz, M., & Flynn, R. (2011). Impact of metacognitive awareness on learning in technology enhanced learning environments. In eTeaching and Learning Workshop, 1 June 2011. London: The University of Greenwich.

  • Cheung, A., & Slavin, R. E. (2011). The effectiveness of education technology for enhancing reading achievement: A meta-analysis. The Center for Research and Reform in Education, Johns Hopkins University: http://www.bestevidence.org/reading/tech/tech.html.

  • Christmann, E. P., & Badgett, J. L. (2000). The comparative effectiveness of CAI on collegiate academic performance. Journal of Computing in Higher Education, 11(2), 91–103. doi:10.1007/BF02940892.

    Article  Google Scholar 

  • Clarke, T., Ayres, P., & Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology Research and Development, 53(3), 15–24.

    Article  Google Scholar 

  • Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558.

    Article  Google Scholar 

  • Cooper, A. (2012). Today’s technologies enhance writing in mathematics. The Clearing House, 85, 80–85. doi:10.1080/00098655.2011.624394.

    Article  Google Scholar 

  • Corbett, A. T., Koedinger, K. R., & Anderson, J. R. (1997). Intelligent tutoring systems. In M. Helander, T. K. Landauer, & P. Prabhu (Eds.), Handbook of human-computer interaction (2nd ed., pp. 849–874). New York: Elsevier.

    Chapter  Google Scholar 

  • Cowan, R., Donlan, C., Newton, E. J., & Lloyd, D. (2005). Number skills and knowledge in children with specific language impairment. Journal of Educational Psychology, 97, 732–744. doi:10.1037/0022-0663.97.4.732.

    Article  Google Scholar 

  • Cowan, R., Donlan, C., Shepherd, D. L., Cole-Fletcher, R., Saxton, M., & Hurry, J. (2011). Basic calculation proficiency and mathematics achievement in elementary school children. Journal of Educational Psychology, 103, 786–803.

    Article  Google Scholar 

  • Crawford, L., Higgins, K. N., & Freeman, B. (2012). Exploring the use of active electronic support tools by students with learning disabilities. Learning Disabilities: A Multidisciplinary Journal, 18(3), 135–144.

    Google Scholar 

  • Crawford, L., Tindal, G., & Stieber, S. (2001). Using oral reading rate to predict student performance on statewide achievement tests. Educational Assessment, 7, 303–323.

    Article  Google Scholar 

  • Donnelly, D. F., Linn, M. C., & Ludvigsen, S. (2014). Impacts and characteristics of computer-based science inquiry learning environments for precollege students. Review of Educational Research, 84(4), 572–608.

    Article  Google Scholar 

  • Durand, M., Hulme, C., Larkin, R., & Snowling, M. (2005). The cognitive foundations of reading and arithmetic skills in 7- to 10-year-olds. Journal of Experimental Child Psychology, 91, 113–136. doi:10.1016/j.jecp.2005.01.003.

    Article  Google Scholar 

  • Emsley, R., Dunn, G., & White, I. R. (2010). Mediation and moderation of treatment effects in randomized controlled trials of complex interventions. Statistical Methods in Medical Research, 19, 237–270.

    Article  Google Scholar 

  • Englert, C., Manalo, M., & Zhao, Y. (2004). I can do it better: The effects of Technology enabled scaffolding on young writers’ composition. Journal of Special Education Technology, 19(1), 5–22.

    Google Scholar 

  • Garcia, L., Nussbaum, M., & Preiss, D. D. (2011). Is the use of information and communication technology related to performance in working memory tasks. Evidence from seventh-grade students. Computers & Education, 57(3), 2068–2076. doi:10.1016/j.compedu.2011.05.009.

    Article  Google Scholar 

  • Gathercole, S. E., & Pickering, S. J. (2000). Working memory deficits in children with low achievements in the national curriculum at 7 years of age. British Journal of Educational Psychology, 70, 177–194.

    Article  Google Scholar 

  • Geary, D. C., & Brown, S. C. (1991). Cognitive addition: Strategy choice and speed-of- processing differences in gifted, normal, and mathematically disabled children. Developmental Psychology, 27(3), 398–406.

    Article  Google Scholar 

  • Geary, D. C., Hoard, M. K., & Hamson, C. O. (1999). Numerical and arithmetical cognition: Patterns of functions and deficits in children at risk for a mathematical disability. Journal of Experimental Child Psychology, 74, 213–239.

    Article  Google Scholar 

  • Geary, D. C., Hoard, M. K., Nugent, L., & Byrd-Craven, J. (2007). Strategy use, long-term memory, and working memory capacity. In D. Berch & M. Mazzocco (Eds.), Why is math so hard for some children? (pp. 83–105). Baltimore, MD: Paul H. Brookes Publishing Co.

    Google Scholar 

  • Goldberg, A., Russell, M., & Cook, A. (2003). The effect of computers on student writing: A meta-analysis of studies from 1992–2002. Journal of Technology, Learning, and Assessment, 2, 3–51.

    Google Scholar 

  • Good, R. H., & Kaminski, R. A. (2002). Dynamic indicators of basic early literacy skills (6th ed.). Eugene, OR: Institute for the Development of Educational Achievement.

    Google Scholar 

  • Grasel, C., & Fischer, F. (2000). Information and communication technologies at schools: A trigger for better teaching and learning? International Journal of Educational Policy, Research, and Practice, 1, 327–336.

    Google Scholar 

  • Horney, M. A., Anderson-Inman, L., Terrazas-Arellanes, F., Schulte, W., Mundorf, J., Smolkowski, K., et al. (2009). Exploring the effects of digital note taking on student comprehension of science texts. Journal of Special Education Technology, 24(3), 45–61.

    Article  Google Scholar 

  • Hsu, Y. C. (2003). The effectiveness of computer-assisted instruction in statistics education: A meta-analysis (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses dataset. (UMI No. 3089963).

  • Israel, M., Maynard, K., & Williamson, P. (2013). Promoting literacy-embedded, authentic STEM instruction for students with disabilities and other struggling learners. Teaching Exceptional Children, 45(4), 18–25.

    Article  Google Scholar 

  • IBM Corp. Released 2010. IBM SPSS Statistics for Windows, Version 19.0. Armonk, NY: IBM Corp.

  • Izzo, M., Yurick, A., & McArrell, B. (2009). Supported eText: Effects of text-to-speech on access and achievement for high school students with disabilities. Journal of Special Education Technology, 22(4), 1–15.

    Google Scholar 

  • Jenkins, J. R., & Jewell, M. (1993). Examining the validity of two measures for formative teaching: Read-aloud and maze. Exceptional Children, 59, 421–432.

    Google Scholar 

  • Jordan, N. C. (2007). Do words count? Connections between mathematics and reading difficulties. In D. B. Berch & M. M. M. Mazzocco (Eds.), Why is math so hard for some children? The nature and origins of mathematical learning difficulties and disabilities (pp. 107–120). Baltimore, MD: Brookes Publishing.

    Google Scholar 

  • Jordan, N. C., & Montani, T. O. (1997). Cognitive arithmetic and problem solving: A comparison of children with specific and general mathematics difficulties. Journal of Learning Disabilities, 30, 624–634. doi:10.1177/002221949703000606.

    Article  Google Scholar 

  • Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilford Press.

    Google Scholar 

  • Kuchler, J. M. (1998). The effectiveness of using computers to teach secondary school (grades 6–12) mathematics: A meta-analysis (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database. (UMI No. 9910293).

  • Lee, H. S., & Hollebrands, K. (2006). Students’ use of technological features while solving mathematics problems. Journal of Mathematical Behavior, 25(3), 252–266.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Linn, M.C. & Slotta, J.D. (2000) WISE Science. Educational Leadership, 52, 29–32. Association for Supervision and Curriculum Development. Alexandria, VA.

  • Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.

    Article  Google Scholar 

  • McGrew, K. S., & Pehl, J. (1988). Prediction of future achievement by the Woodcock-Johnson Psycho-Educational Battery and the WISC-R. Journal of School Psychology, 26(3), 275–281.

    Article  Google Scholar 

  • McLean, J. F., & Hitch, G. J. (1999). Working memory in children with specific learning disabilities. Journal of Experimental Child Psychology, 74, 240–260.

    Article  Google Scholar 

  • Moos, D. C., & Azevedo, R. (2009). Learning with computer-based learning environments: A literature review of computer self-efficacy. Review of Educational Research, 79(2), 576–600.

    Article  Google Scholar 

  • Myers, J., & Beach, R. (2004). Constructing critical literacy practices through technology tools and inquiry. Contemporary Issues in Technology and Teacher Education, 4(3), 257–268.

    Google Scholar 

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

    Google Scholar 

  • National Governors Association Center for Best Practices & Council of Chief State School Officers. (2010). Common Core State Standards for Mathematics. Washington, DC: Authors.

    Google Scholar 

  • National Mathematics Advisory Panel. (2008). Foundations for success: The final report of the national mathematics advisory panel. Washington, DC: U. S. Department of Education.

    Google Scholar 

  • National Science Foundation. (2011). Empowering the nation through discover and innovation: NSF strategic plan for fiscal years (FY) 2011–2016. April. NSF 11-047.

  • Nicolaou, C. T., Nicolaidou, I., Zacharia, Z., & Constantinou, C. P. (2007). Enhancing fourth graders’ ability to interpret graphical representations through the use of microcomputer-based labs implemented within an inquiry-based activity sequence. Journal of Computers in Mathematics and Science Teaching, 26(1), 75–99.

    Google Scholar 

  • Noeth, R. J., & Volkov, B. B. (2004). Evaluating the effectiveness of technology in our schools. ACT policy report. American College Testing ACT Inc. Retrieved from http://www.act.org/research/policymakers/pdf/school_tech.pdf.

  • Olson, R., Foltz, G., & Wise, B. (1986). Reading instruction and remediation with the aid of computer speech. Behavior Research Methods: Instruments and Computers, 18, 93–99.

    Article  Google Scholar 

  • Panaoura, A. (2006). The development of young pupils’ self-representation and mathematical performance in relation to processing efficiency and working memory. Educational Psychology: An International Journal of Experimental Educational Psychology, 26(5), 643–676.

    Article  Google Scholar 

  • Raghubar, K. P., Barnes, M. A., & Hecht, S. A. (2010). Working memory and mathematics: A review of developmental, individual difference, and cognitive approaches. Learning and Individual Differences, 20, 110–122.

    Article  Google Scholar 

  • Rasmussen, C., & Bisanz, J. (2011). The relation between mathematics and working memory in young children with Fetal Alcohol Spectrum Disorders. The Journal of Special Education, 45(3), 184–191.

    Article  Google Scholar 

  • Reitsma, P. (1988). Reading practice for beginners: Effects of guided reading, reading while listening, and independent reading with computer-based speech feedback. Reading Research Quarterly, 23, 219–235.

    Article  Google Scholar 

  • Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12, 529–556.

    Article  Google Scholar 

  • Roblyer, M. D., Castine, W. H., & King, F. J. (1988). Assessing the impact of computer-based instruction: A review of recent research. Computers in the Schools, 5(3–4), 41–68. doi:10.1300/J025v05n03_04.

    Article  Google Scholar 

  • Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011). Improving students’ help-seeking skills using meta-cognitive feedback in an intelligent tutoring system. Learning and Instruction, 21, 267–280.

    Article  Google Scholar 

  • 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, 1–14.

    Article  Google Scholar 

  • Ryan, A. M., & Shin, H. (2011). Help-seeking tendencies: An examination of motivational correlates and consequences for achievement during the first year of middle school. Learning and Instruction, 21, 247–256.

    Article  Google Scholar 

  • Schacter, D. (1999). The seven sins of memory: Insights from psychology and cognitive neuroscience. American Psychologist, 54(3), 182–203.

    Article  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 Dissertations and Theses database. (UMI NO. 3286857).

  • Shute, V. J., & Gluck, K. A. (1996). Individual differences in patterns of spontaneous online tool use. The Journal of the Learning Sciences, 5(4), 329–355.

    Article  Google Scholar 

  • Shute, V. J., & Psotka, J. (1996). Intelligent tutoring systems: Past, present, and future. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 570–600). New York: Macmillan.

    Google Scholar 

  • Slavin, R., & Lake, C. (2009). Effective programs in elementary mathematics: A best-evidence synthesis. Review of Educational Research, 78, 427–515.

    Article  Google Scholar 

  • Swanson, H. L. (1992). Generality and modifiability of working memory among skilled and less skilled readers. Journal of Educational Psychology, 84, 473–488. doi:10.1037/0022-0663.84.4.473.

    Article  Google Scholar 

  • Swanson, H. L. (2011). Test of working memory: Abbreviated test. Unpublished instrument.

  • Swanson, H. L., & Sachse-Lee, C. (2001). Mathematical problem solving and working memory in children with learning disabilities: Both executive and phonological processes are important. Journal of Experimental Child Psychology, 79, 294–321.

    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, 4–28.

    Article  Google Scholar 

  • Time4Learning©. (2013). Retrieved from http://www.time4learning.com.

  • Tran, Z. (2005). Help with English language proficiency “HELP” program evaluation of sheltered instruction multimedia lessons. Retrieved from www.helpprogram.net.

  • Vukovic, R. K., & Lesaux, N. K. (2013). The language of mathematics: Investigating the ways language counts for children’s mathematical development. Journal of Experimental Child Psychology, 115, 227–244. doi:10.1016/j.jecp.2013.02.002.

    Article  Google Scholar 

  • Wilson, R., Majsterek, D., & Simmons, D. (1996). The effects of computer-assisted and teacher-directed instruction on the multiplication performance of Elementary students with learning disabilities. Journal of Learning Disabilities, 29(4), 382–390.

    Article  Google Scholar 

  • Winne, P. H., Nesbit, J. C., Kumar, V., Hadwin, A. F., Lajoie, S. P., Azevedo, R., et al. (2006). Supporting self-regulated learning with gStudy software: The learning kit project. Technology, Instruction, Cognition and Learning, 3, 105–113.

    Google Scholar 

  • Wood, H., & Wood, D. (1999). Help seeking, learning and contingent tutoring. Computers & Education, 33, 153–169.

    Article  Google Scholar 

  • Zemelman, S., Daniels, H., & Hyde, A. (2012). Best practice: Today’s standards for teaching and learning in America’s schools. Portsmouth, NH: Heinemann.

    Google Scholar 

  • Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183.

    Article  Google Scholar 

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Acknowledgments

Preparation of this article was supported by the Mathematics eText Research Center (MeTRC), University of Oregon. MeTRC is supported in part by the U.S. Department of Education, Office of Special Education Programs, CFDA 84.327H.

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Authors and Affiliations

  1. Texas Christian University, Box 297900, Fort Worth, TX, 76129, USA

    Lindy Crawford

  2. Tarleton State University, Stephenville, TX, USA

    Kristina N. Higgins

  3. University of Nebraska-Lincoln, Lincoln, NE, USA

    Jacqueline N. Huscroft-D’Angelo

  4. Four Square Church, Dallas, TX, USA

    Lindsay Hall

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Crawford, L., Higgins, K.N., Huscroft-D’Angelo, J.N. et al. Students’ use of electronic support tools in mathematics. Education Tech Research Dev 64, 1163–1182 (2016). https://doi.org/10.1007/s11423-016-9452-7

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