Implementation fidelity and attainment in computerized practice of mathematics

References

• Anamourlis, A. 2001. The Impact of the Accelerated Maths Pilot Program in Australia. Boxhill: Renaissance Learning Australia.
   Google Scholar
• Betebenner, D. W. 2011. A Technical Overview of the Student Growth Percentile Methodology: Student Growth Percentiles and Percentile Growth Projections/Trajectories. Dover, New Hampshire: National Center for the Improvement of Educational Assessment. Accessed 14 June 2016. http://www.nj.gov/education/njsmart/performance/SGP_Technical_Overview.pdf
   Google Scholar
• Bolt, D., J. Ysseldyke, and M. Patterson. 2010. “Students, Teachers, and Schools as Sources of Variability, Integrity, and Sustainability in Implementing Progress Monitoring.” School Psychology Review 39 (4): 612–630.
   Web of Science ®Google Scholar
• Burns, M. K., D. A. Klingbeil, and J. Ysseldyke. 2010. “The Effects of Technology Enhanced Formative Evaluation on Student Performance on State Accountability Math Tests.” Psychology in the Schools 47 (6): 582–591. doi:10.1002/pits.v47:6.
   Web of Science ®Google Scholar
• Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences. New York and London: Routledge. ISBN 1-134-74270-3.
   Google Scholar
• Crawford, L., D. M. Carpenter, M. T. Wilson, M. Schmeister, and M. McDonald. 2012. “Testing the Relation between Fidelity of Implementation and Student Outcomes in Math.” Assessment for Effective Intervention 37 (4): 224–235. doi:10.1177/1534508411436111.
   Google Scholar
• Gaeddert, T. 2001. “Using Accelerated Math to Enhance Student Achievement in High School Mathematics Courses.” Unpublished master’s thesis, Friends University, Wichita, Kansas. ERIC Number: ED463177.
   Google Scholar
• Holmes, C. T., C. L. Brown, and B. Algozzine. 2006. “Promoting Academic Success for All Students.” Academic Exchange Quarterly 10 (3): 141–147.
   Google Scholar
• Holstein, K. A. 2012. “A Characterization of Teachers‘ Implementations of a Mathematical Decision-Making Curriculum.” ProQuest LLC, Ph.D. Dissertation, North Carolina State University. ERIC Number: ED550966.
   Google Scholar
• Kinzie, M. B., J. V. Whittaker, P. McGuire, Y. J. Lee, and C. Kilday. 2015. “Research on Curricular Development for Pre-Kindergarten Mathematics and Science.” Teachers College Record 117 (7): 136–157.
   Web of Science ®Google Scholar
• Knock, D. J. 2005. Nottingham Pupils Improve Mathematics Achievement with Accelerated Maths. Wisconsin Rapids, WI: Renaissance Learning.
   Google Scholar
• Lambert, R., B. Algozzine, and J. McGee. 2014. “Effects of Progress Monitoring on Math Performance of At-Risk Students.” British Journal of Education, Society and Behavioural Science 4 (4): 527–540. http://www.journalrepository.org/media/journals/BJESBS_21/2014/Jan/Lambert442013BJESBS7259_1.pdf
   Google Scholar
• Lave, J., and E. Wenger. 1990. Situated Learning: Legitimate Peripheral Participation. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Lehman, R. H., and S. Seeber. 2005. Accelerated Mathematics in Grades 4–6. Wisconsin Rapids, WI: Renaissance Learning.
   Google Scholar
• Lekwa, A. J. 2012. “Technology-Enhanced Formative Assessment in Mathematics for English Language Learners.” Unpublished doctoral dissertation, University of Minnesota, Minneapolis. ERIC Number: ED551876.
   Google Scholar
• Nunnery, J. A., and S. M. Ross. 2007. “The Effects of the School Renaissance Program on Student Achievement in Reading and Mathematics.” Research in the Schools 14 (1): 40–59.
   Google Scholar
• Randel, B., H. Apthorp, A. D. Beesley, T. F. Clark, and X. Wang. 2016. “Impacts of Professional Development in Classroom Assessment on Teacher and Student Outcomes.” Journal of Educational Research 109 (5): 491–502. doi:10.1080/00220671.2014.992581.
   Web of Science ®Google Scholar
• Renaissance Learning. 2013. The Research Foundation for Star Assessments. Wisconsin Rapids, WI: Renaissance Learning.
   Google Scholar
• Rudd, P., and P. Wade. 2006. Evaluation of Renaissance Learning Mathematics and Reading Programs in UK Specialist and Feeder Schools. Slough: National Foundation for Educational Research.
   Google Scholar
• Sawilowsky, S. 2009. “New Effect Size Rules of Thumb.” Journal of Modern Applied Statistical Methods 8 (2): 467–474. doi:10.22237/jmasm/1257035100.
   Google Scholar
• Schulte, A. C., J. E. Easton, and J. Parker. 2009. “Advances in Treatment Integrity Research: Multidisciplinary Perspectives on the Conceptualization, Measurement, and Enhancement of Treatment Integrity.” School Psychology Review 38: 460–475.
   Web of Science ®Google Scholar
• Shang, Y., A. VanIwaarden, and D. W. Betebenner. 2015. “Covariate Measurement Error Correction for Student Growth Percentiles Using the SIMEX Method.” Educational Measurement: Issues and Practice 34 (1): 4–14. doi:10.1111/emip.12058.
   Web of Science ®Google Scholar
• Spicuzza, R., J. Ysseldyke, A. Lemkuil, S. Kosciolek, C. Boys, and E. Teelucksingh. 2001. “Effects of Curriculum-Based Monitoring on Classroom Instruction and Math Achievement.” Journal of School Psychology 39 (6): 521–542. doi:10.1016/S0022-4405(01)00087-5.
   Web of Science ®Google Scholar
• Springer, R. M., D. Pugalee, and B. Algozzine. 2007. “Improving Mathematics Skills of High School Students.” The Clearing House 81 (1): 37–44. doi:10.3200/TCHS.81.1.37-44.
   Google Scholar
• Stanley, A. M. 2011. “Accelerated Mathematics and High-Ability Students‘ Math Achievement in Grades Three and Four.” ProQuest LLC, Ed.D. Dissertation, East Tennessee State University. ERIC Number: ED532232.
   Google Scholar
• Teelucksingh, E., J. Ysseldyke, R. Spicuzza, and M. Ginsburg-Block. 2001. Enhancing the Learning of English Language Learners: Consultation and a Curriculum-Based Monitoring System. Minneapolis, MN: University of Minnesota, National Center for Educational Outcomes.
   Google Scholar
• Topping, K. J. 2017. “Implementation Fidelity in Computerised Assessment of Book Reading.” Computers and Education 116: 176–190. doi:10.1016/j.compedu.2017.09.009.
   Web of Science ®Google Scholar
• Topping, K. J. 2018. “Implementation Fidelity and Pupil Achievement in Book Reading: Variation between Regions, Local Authorities and Schools.” Research Papers in Education 33 (5): 620–641. doi:10.1080/02671522.2017.1329340.
   Web of Science ®Google Scholar
• Walker Driesel, D. 2013. “Mathematics Interventions: A Correlational Study of the Relationship Between Level of Implementation of the Accelerated Math Program and Student Achievement.” ProQuest LLC, Ed.D. Dissertation, Liberty University. ERIC Number: ED564886.
   Google Scholar
• What Works Clearinghouse. 2017. Accelerated Math. Primary Mathematics. Washington, DC: Institute of Education Sciences.
   Google Scholar
• Wolfe, C. B., D. H. Clements, J. Sarama, and M. E. Spitler. 2013. Sustainability of Fidelity of Implementation over Time in the Context of a Prekindergarten Mathematics Curriculum and Professional Development Scale-Up Intervention. Washington, DC: Society for Research on Educational Effectiveness. ERIC Number: ED564101. Accessed 12 June 2018. https://files.eric.ed.gov/fulltext/ED564101.pdf
   Google Scholar
• Wright, S. P. 2010. An Investigation of Two Nonparametric Regression Models for Value-Added Assessment in Education. Cary, NC: SAS Institute. Accessed 16 June 2016. https://education.ohio.gov/getattachment/Topics/Data/Report-Card-Resources/Ohio-Report-Cards/Value-Added-Technical-Reports-1/An-Investigation-of-Two-Nonparametric-Regression-Models-for-Value-Added-Assessment-in-Education-S-Paul-Wright-1.pdf.aspx
   Google Scholar
• Ysseldyke, J., and J. Betts. 2010. “Progress Monitoring, Implementation Integrity, and Guided Practice across Multiple Math Curricula.” Presentation at Council for Exceptional Children 2010 Annual Convention & Expo, Nashville, TN.
   Google Scholar
• Ysseldyke, J., J. Betts, T. Thill, and E. Hannigan. 2004a. “Use of an Instructional Management System to Improve Mathematics Skills for Students in Title I Programs.” Preventing School Failure 48 (4): 10–14. doi:10.3200/PSFL.48.4.10-14.
   Google Scholar
• Ysseldyke, J., and D. Bolt. 2007. “Effect of Technology-Enhanced Continuous Progress Monitoring on Math Achievement.” School Psychology Review 36 (3): 453–467.
   Web of Science ®Google Scholar
• Ysseldyke, J., R. Spicuzza, S. Kosciolek, and C. Boys. 2003a. “Effects of a Learning Information System on Mathematics Achievement and Classroom Structure.” Journal of Educational Research 96 (3): 163–173. doi:10.1080/00220670309598804.
   Web of Science ®Google Scholar
• Ysseldyke, J., R. Spicuzza, S. Kosciolek, E. Teelucksingh, C. Boys, and A. Lemkuil. 2003b. “Using a Curriculum-Based Instructional Management System to Enhance Math Achievement in Urban Schools.” Journal of Education for Students Placed at Risk 8 (2): 247–265. doi:10.1207/S15327671ESPR0802_4.
   Google Scholar
• Ysseldyke, J., and S. Tardrew. 2007. “Use of a Progress-Monitoring System to Enable Teachers to Differentiate Mathematics Instruction.” Journal of Applied School Psychology 24 (1): 1–28. doi:10.1300/J370v24n01_01.
   Google Scholar
• Ysseldyke, J., S. Tardrew, J. Betts, T. Thill, and E. Hannigan. 2004b. “Use of an Instructional Management System to Enhance Math Instruction of Gifted and Talented Students.” Journal for the Education of the Gifted 27 (4): 293–310. doi:10.4219/jeg-2004-319.
   Web of Science ®Google Scholar
• Zumwalt, D. B. 2001. “The Effectiveness of Computer-Aided Instruction in Eighth-Grade Pre-Algebra Classrooms in Idaho.” Unpublished PhD dissertation, Idaho State University, Idaho. Accessed 12 June 2018 https://www.learntechlib.org/p/117250/
   Google Scholar