We examined a web-based mathematics progress monitoring tool for second graders. The tool monitors the learning progress of two competences, number sense and computation. A total of 414 students from 19 classrooms in Germany were checked every 3 weeks from fall to spring. Correlational analyses indicate that alternate-form reliability was adequate for the chosen interval (.81 < r < .87). Results from latent growth curve modeling (LGCM) identified significant linear increases in students’ scores, and a significant variance in computation slopes was observed. Criterion validity coefficients, comparing the measures with student performance on standardized school achievement tests at the beginning and the end of the school year (DEMAT1+, DEMAT2+), were satisfactory (.59 < r < .76). Students conducted the tests independently, and assessment times were short. Implications for further research and classroom practice are discussed.
AllinderR. M.BeckbestM. A. (1995). Differential effects of two approaches to supporting teachers’ use of curriculum-based measurement. School Psychology Review, 24, 287-298.
2.
ArdoinS. P.ChristT. J.MorenaL. S.CormierD. C.KlingbeilD. A. (2013). A systematic review and summarization of the recommendations and research surrounding curriculum-based measurement of oral reading fluency (CBM-R) decision rules. Journal of School Psychology, 51, 1-18. doi:10.1016/j.jsp.2012.09.004
3.
BerchD. B. (2005). Making sense of number sense: Implications for children with mathematical disabilities. Journal of Learning Disabilities, 38, 333-339. doi:10.1177/00222194050380040901
4.
BlackP.WiliamD. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5, 7-74.
5.
BlackP.WiliamD. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21, 5-31.
6.
BodovskiK.FarkasG. (2007). Mathematics growth in early elementary school: The roles of beginning knowledge, student engagement, and instruction. The Elementary School Journal, 108, 115-130. doi:10.1086/525550
7.
BollenK. A.CurranP. J. (2006). Latent curve models: A structural equation perspective. New York, NY: Wiley.
8.
ChardD. J.ClarkeB.BakerS. K.OtterstedtJ.BraunD.KatzR. (2005). Using measures of number sense to screen for difficulties in mathematics: Preliminary findings. Assessment for Effective Intervention, 30(2), 3-14. doi:10.1177/073724770503000202
9.
ChristT. J.ZopluogluC.MonaghenB. D.Van NormanE. R. (2013). Curriculum-based measurement of oral reading: Multi-study evaluation of schedule, duration, and dataset quality on progress monitoring outcomes. Journal of School Psychology, 51, 19-57. doi:10.1016/j.jsp.2012.11.001
10.
ClarkeB.BakerS. K.SmolkowskiK.ChardD. J. (2008). An analysis of early numeracy curriculum-based measurement: Examining the role of growth in student outcomes. Remedial and Special Education, 29, 46-57. doi:10.1177/0741932507309694
11.
ClauseC. S.MullinsM. E.NeeM. T.PulakosE.SchmittN. (1998). Parallel test form development: A procedure for alternate predictors and an example. Personnel Psychology, 51, 193-208.
12.
ClementsD. H. (2004). Geometric and spatial thinking in early childhood education. In SaramaJ.ClementsD. H.DiBiaseA.-M. (Eds.), Engaging young children in mathematics: Standards for early childhood mathematics education (pp. 267-298). Mahwah, NJ: Lawrence Erlbaum Associates.
13.
CohenJ.CohenP.WestS. G.AikenL. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, NJ: Erlbaum.
14.
CollinsL. M.SchaferJ. L.KamC.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351. doi:10.1037/1082-989X.6.4.330
15.
ConnorC. M.MorrisonF. J.PetrellaJ. N. (2004). Effective reading comprehension instruction: Examining child x instruction interactions. Journal of Educational Psychology, 96, 682-698. doi:10.1037/0022-0663.96.4.682
16.
DehaeneS. (1992). Varieties of numerical abilities. Cognition, 44, 1-42.
17.
DehaeneS. (2001). Précis of the number sense. Mind & Language, 16, 16-36. doi:10.1111/1468-0017.00154
18.
DehaeneS. (2011). The number sense: How the mind creates mathematics (2nd ed.). New York, NY: Oxford University Press.
19.
DehaeneS.CohenL. (1995). Towards an anatomical and functional model of number processing. Mathematical Cognition, 1(1), 83-120.
20.
DehaeneS.PiazzaM.PinelP.CohenL. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20, 487-506. doi:10.1080/02643290244000239
21.
DenoS. L. (2003). Curriculum-based measures: Development and perspectives. Assessment for Effective Intervention, 28(3-4), 3-11. doi:10.1177/073724770302800302
22.
EndersC. K. (2001). A primer on maximum likelihood algorithms available for use with missing data. Structural Equation Modeling, 8, 128-141.
23.
FoegenA.JibanC. L.DenoS. L. (2007). Progress monitoring measures in mathematics: A review of the literature. Journal of Special Education, 41, 121-139. doi:10.1177/00224669070410020101
24.
FörsterN.SouvignierE. (2011). Curriculum-based measurement: Developing a computer-based assessment instrument for monitoring student reading progress on multiple indicators. Learning Disabilities, 9(2), 65-88.
25.
FuchsL. S. (2004). The past, present, and future of curriculum-based measurement research. School Psychology Review, 33, 188-192.
26.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. doi:10.1080/10705519909540118
27.
JenkinsJ. R.GraffJ. J.MigliorettiD. L. (2009). Estimating reading growth using intermittent CBM progress monitoring. Exceptional Children, 75(2), 151-163.
28.
JordanN. C.KaplanD.LocuniakM. N.RamineniC. (2007). Predicting first-grade math achievement from developmental number sense trajectories. Learning Disabilities Research & Practice, 22, 36-46. doi:10.1111/j.1540-5826.2007.00229.x
29.
JordanN. C.KaplanD.OláhL. N.LocuniakM. N. (2006). Number sense growth in kindergarten: A longitudinal investigation of children at risk for mathematics difficulties. Child Development, 77, 153-175. doi:10.2307/3696696
30.
KrajewskiK. (2008). Prävention der Rechenschwäche [The early prevention of math problems]. In SchneiderW.HasselhornM. (Eds.), Handbuch der Pädagogischen Psychologie (pp. 360-370). Göttingen, Germany: Hogrefe.
31.
KrajewskiK.KüspertP.SchneiderW. (2002). DEMAT1+. Deutscher Mathematiktest für erste Klassen [DEMAT1+. German mathematics test for first grades]. Göttingen, Germany: Hogrefe.
32.
KrajewskiK.LiehmS.SchneiderW. (2004). DEMAT2+. Deutscher Mathematiktest für zweite Klassen [DEMAT2+. German mathematics test for second grades]. Göttingen, Germany: Hogrefe.
33.
KrajewskiK.SchneiderW. (2009). Early development of quantity to number-word linkage as a precursor of mathematical school achievement and mathematical difficulties: Findings from a four-year longitudinal study. Learning and Instruction, 19, 513-526. doi:10.1016/j.learninstruc.2008.10.002
34.
LocuniakM. N.JordanN. C. (2008). Using kindergarten number sense to predict calculation fluency in second grade. Journal of Learning Disabilities, 41, 451-459. doi:10.1177/0022219408321126
35.
MissallK. N.MercerS. H.MartínezR. S.CasebeerD. (2012). Concurrent and longitudinal patterns and trends in performance on early numeracy curriculum-based measures in kindergarten through third grade. Assessment for Effective Intervention, 37, 95-106. doi:10.1177/1534508411430322
36.
MuthénL.MuthénB. O. (1998-2010). Mplus user’s guide. Los Angeles, CA: Author.
SalaschekM.SouvignierE. (2013). Web-based progress monitoring in first grade mathematics. Frontline Learning Research, 1, 53-69.
39.
SchaferJ. L.GrahamJ. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177. doi:10.1037/1082-989X.7.2.147
40.
Schermelleh-EngelK.MoosbruggerH.MüllerH. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.
41.
ShapiroE. S. (2013). Commentary on progress monitoring with CBM-R and decision making: Problems found and looking for solutions. Journal of School Psychology, 51, 59-66. doi:10.1016/j.jsp.2012.11.003
42.
SieglerR. S.BoothJ. L. (2004). Development of numerical estimation in young children. Child Development, 75, 428-444. doi:10.1111/j.1467-8624.2004.00684.x
43.
SteckerP. M.FuchsL. S. (2000). Effecting superior achievement using curriculum-based measurement: The importance of individual progress monitoring. Learning Disabilities Research & Practice, 15, 128-134. doi:10.1207/SLDRP1503_2
44.
SteckerP. M.FuchsL. S.FuchsD. (2005). Using curriculum-based measurement to improve student achievement: Review of research. Psychology in the Schools, 42, 795-819. doi:10.1002/pits.20113
45.
WangS.McCallM.JiaoH.HarrisG. (2013). Construct validity and measurement invariance of computerized adaptive testing: Application to measures of academic progress (MAP) using confirmatory factor analysis. Journal of Educational and Developmental Psychology, 3, 88-100.
46.
YsseldykeJ.BoltD. M. (2007). Effect of technology-enhanced continuous progress monitoring on math achievement. School Psychology Review, 36, 453-467.
