The current study reviews a computer-based math fluency intervention with 216 third- and fourth-grade students who were at risk for math difficulties. The intervention used a computer software program to practice math facts an average of three times per week for 8 to 15 weeks. Data were compared to those of 226 students in a control group. Results indicated that students who participated in the intervention had significantly larger gains on their math scores than those in the control group, and students with severe math problems (at or below the 15th percentile) grew at a rate that was equal to that of students with a pretest score that was between the 15th and 25th percentiles. Moreover, significantly fewer students remained at risk for math failure in the intervention group after participating in the intervention. These data suggest that the computer-based intervention was a useful supplemental math intervention. Suggestions for future research are provided.
BakerS.GerstenR.LeeD. S. (2002). A synthesis of empirical research on teaching mathematics to low-achieving students. Elementary School Journal, 103, 51–73.
2.
BatscheG.ElliottJ.GradenJ. L.GrimesJ.KovaleskiJ. F.PrasseD.. . . TillyW. D.III. (2005). Response to intervention: Policy considerations and implementation. Alexandria, VA: National Association of State Directors of Special Education.
3.
BinderC. (1996). Behavioral fluency: Evolution of a new paradigm. Behavior Analyst, 19, 163–197.
4.
BurnsM. K. (2005). Using incremental rehearsal to practice multiplication facts with children identified as learning disabled in mathematics computation. Education and Treatment of Children, 28, 237–249.
5.
BurnsM.GibbonsK. (2008). Implementing response to intervention procedures in elementary and secondary schools: Procedures to assure scientific-based practice. New York, NY: Routledge.
6.
BurnsM. K.Hall-LandeJ.LymanW.RogersC.TanC. S. (2006). Tier II interventions within response-to-intervention: Components of an effective approach. Communiqué, 35(4), 38–40.
7.
BurnsM. K.VanDerHeydenA. M.BoiceC. H. (2008). Best practices in intensive academic interventions. In ThomasA.GrimesJ. (Eds.), Best practices in school psychology V (pp. 1151–1162). Bethesda, MD: National Association of School Psychologists.
8.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
9.
DalyE. J.HintzeJ. M.HamlerK. R. (2000). Improving practice by taking steps toward technological improvements in academic interventions in the new millennium. Psychology in the Schools, 37, 61–72.
10.
DehaeneS.AkhaveinR. (1995). Attention, automaticity, and levels of representation in number processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 314–326.
11.
FuchsL. S. (2003). Assessing intervention responsiveness: Conceptual and technical issues. Learning Disabilities Research & Practice, 18, 172–186.
12.
FuchsL. S.FuchsD.KarnsK. (2001). Enhancing kindergarteners’ mathematical development: Effects of peer-assisted learning strategies. Elementary School Journal, 101, 495–510.
13.
GarnerM. L. (2005). Test review of Star Math 2.0. In SpiesR. A.PlakeB. S. (Eds.), The sixteenth mental measurements yearbook. Lincoln, NE: Buros Institute of Mental Measurements.
14.
GearyD. C.HoardM. K.Byrd-CravenJ.NugentL.NumteeC. (2007). Cognitive mechanisms underlying achievement deficits in children with mathematical learning disability. Child Development, 78, 1343–1359.
15.
HanichL.JordanN. C.KaplanD.DickJ. (2001). Performance across different areas of mathematical cognition in children with learning difficulties. Journal of Educational Psychology, 93, 615–626.
16.
HaringN. G.EatonM. D. (1978). Systematic instructional technology: An instructional hierarchy. In HaringN. G.LovittT. C.EatonM. D.HansenC. L. (Eds.), The fourth R: Research in the classroom (pp. 23–40). Columbus, OH: Merrill.
17.
HiebertJ.LefevreP. (1986). Conceptual and procedural knowledge in mathematics: An introductory analysis. In HiebertJ. (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 1–27). Hillsdale, NJ: Lawrence Erlbaum.
18.
HolmesC. T.BrownC. L.AlgozzineB. (2006). Promoting academic success for all students. Academic Exchange Quarterly, 10, 141–147.
19.
HouchinsD. E.ShippenM. E.FloresM. M. (2004). Math assessment and instruction for students at-risk. In ColarussoR.O’RourkeC. (Eds.), Special education for all teachers (3rd ed., pp. 319–357). Dubuque, IA: Kendall/Hunt.
20.
JimenezJ. E.OrtizM. D. R.RodrigoM.Hernandez-ValleI.RamirezG.EstevezA.. . . TrabaueM. (2003). Do the effects of computer-assisted practice differ for children with reading disabilities with and without IQ-achievement discrepancy?Journal of Learning Disabilities, 36, 34–47.
21.
KilpatrickJ.SwaffordJ.FindellB. (2001). Adding it up: Helping children learn mathematics. Washington, DC: National Academies Press.
22.
LoganG. D.TaylorS. E.EthertonJ. L. (1996). Attention in the acquisition and expression of automaticity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 620–638.
23.
MazzoccoM. M. (2007). Defining and differentiating mathematical learning disabilities and difficulties. In BerchD. B.MazzoccoM. M. (Eds.), Why is math so hard for some children? The nature and origins of mathematical learning difficulties and disabilities (pp. 29–48). Baltimore, MD: Brookes.
24.
McIntireT. (2002). The administrator’s guide to data-driven decision making. Technology & Learning, 22(11), 18–28, 32–33.
25.
National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author.
26.
National Mathematics Advisory Panel. (2008). Foundations for success: The final report of the National Mathematics Advisory Panel. Washington, DC: U.S. Department of Education.
27.
PoncyB. C.SkinnerC. H. (2006). Detect, practice, and repair: The effects of a classwide intervention on elementary students’ math-fact fluency. Journal of Evidence-Based Practices for Schools, 7, 69–72.
28.
Renaissance Learning. (2002). Star Math: Version 2.0. Wisconsin Rapids, WI: Author.
29.
Renaissance Learning. (2003). Math Facts in a Flash. Wisconsin Rapids, WI: Author.
30.
Rittle-JohnsonB.SieglerR. S. (1998). The relation between conceptual and procedural knowledge in learning mathematics: A review. In DonlanC. (Ed.), The development of mathematical skill (pp. 75–110). Hove, UK: Psychology Press.
31.
Rittle-JohnsonB.SieglerR. S.WagnerM. A. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93, 346–362.
32.
RiveraD. M.BryantB. R. (1992). Mathematics instruction for students with special needs. Intervention in School and Clinic, 28, 71–86.
33.
SafferN. (1999). Core subjects and your career. Occupational Outlook Quarterly, 43(2), 26–40.
34.
SamuelsS. J. (1987). Information processing abilities and reading. Journal of Learning Disabilities, 20, 18–22.
35.
ShinnM. (2005). Identifying and validating academic problems in a problem-solving model. In Brown-ChidseyR. (Ed.), Assessment for intervention (pp. 219–246). New York, NY: Guilford.
36.
Singer-DudekJ.GreerR. D. (2005). Long-term analysis of the relationship between fluency and the training and maintenance of complex math skills. Psychological Record, 55, 361–376.
37.
SpringerR. M.PugaleeD.AlgozzineB. (2007). Improving mathematics skills in high school students. Clearing House, 81, 37–44.
38.
SzadokierskiI.BurnsM. K. (2008). Analogue evaluation of the effects of opportunities to respond and ratios of known items within drill rehearsal of Esperanto words. Journal of School Psychology, 46, 593–609.
39.
TorgesenJ. K.AlexanderA. W.WagnerR. K.RashotteC. A.VoellerK. K. S.ConwayT. (2001). Intensive remedial instruction for children with severe reading disabilities: Immediate and long-term outcomes for two instructional approaches. Journal of Learning Disabilities, 34, 33–58.
40.
VanDerHeydenA. M.BurnsM. K. (2005). Using curriculum-based assessment and curriculum-based measurement to guide elementary mathematics instruction: Effect on individual and group accountability scores. Assessment for Effective Intervention, 30(3), 15–29.
41.
VaughnS.WanzekJ.Linan-ThompsonS.MurrayC. (2007). Monitoring response to intervention for students at-risk for reading difficulties: High and low responders. In JimersonS. R.BurnsM. K.VanDerHeydenA. M. (Eds.), The handbook of response to intervention: The science and practice of assessment and intervention (pp. 234–243). New York, NY: Springer.
42.
VellutinoF. R.ScanlonD. M.SipayE. R.SmallS.ChenR.PrattA.DenklaM. B. (1996). Cognitive profiles of difficulty-to-remediate and readily remediated poor readers: Early intervention as a vehicle for distinguishing between cognitive and experimental deficits as basic causes of specific reading disability. Journal of Educational Psychology, 88, 601–638.
43.
WaymanJ. C. (2005). Involving teachers in data-driven decision-making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed At Risk, 10, 295–308.
44.
YsseldykeJ.BoltD. (2007). Effect of technology-enhanced continuous progress monitoring on math achievement. School Psychology Review, 36, 453–467.
45.
YsseldykeJ. E.McLeodS. (2007). Using technology tools to monitor response to intervention. In JimersonS.BurnsM.VanderHeydenA. (Eds.), Handbook of response to intervention (pp. 396–407). New York, NY: Springer.
46.
YsseldykeJ.ThrillT.PohlJ.BoltD. (2005). Using Math Facts in a Flash to enhance computational fluency. Journal of Evidence-Based Practices for Schools, 6, 59–89.
