Designing Effective Math Interventions

Math Intervention Strategies

Examining the Potential for Response to Intervention (RTI) Delivery Models in Secondary Education - Advances in Higher Education and Professional Development ◽

10.4018/978-1-5225-2187-7.ch003 ◽

2017 ◽

pp. 46-56

Keyword(s):

High School ◽

Intervention Strategies ◽

Literacy Skills ◽

Math Skills ◽

Math Intervention ◽

Math Interventions

Like with literacy skills, many students will enter middle or high school lacking the fundamental math skills needed to be successful, and they will need to learn them before moving forward. This chapter focuses on describing proven research-based math interventions that can assist with this instruction. In addition, current research on these interventions is presented.

Download Full-text

Meta-Analysis of Acquisition and Fluency Math Interventions With Instructional and Frustration Level Skills: Evidence for a Skill-by-Treatment Interaction

School Psychology Review ◽

10.1080/02796015.2010.12087791 ◽

2010 ◽

Vol 39 (1) ◽

pp. 69-83 ◽

Cited By ~ 33

Author(s):

Matthew K. Burns ◽

Robin S. Codding ◽

Christina H. Boice ◽

G. Lukito

Keyword(s):

Meta Analysis ◽

Math Interventions ◽

Treatment Interaction ◽

Frustration Level

Download Full-text

Brief Experimental Analysis of Math Interventions: A Synthesis of Evidence

Assessment for Effective Intervention ◽

10.1177/1534508419883937 ◽

2019 ◽

pp. 153450841988393

Author(s):

Nicole M. McKevett ◽

Robin S. Codding

Keyword(s):

Experimental Analysis ◽

Reading Interventions ◽

Brief Experimental Analysis ◽

Quick Method ◽

Math Intervention ◽

Math Interventions ◽

Computational Fluency ◽

Effective Interventions ◽

And Performance ◽

Performance Interventions

Brief experimental analysis (BEA) is a quick method used to identify the function of student learning difficulties and match effective interventions to students’ needs. Extensive work has been done to explore the use of this methodology to determine effective reading interventions; however, a smaller number of published studies have examined the use of BEAs in math. The purpose of the current review was to identify all studies that have used BEA methodology in math. Fifteen studies that included 63 participants and used BEA methodology to identify the most effective math intervention for students were located. Results of the synthesis indicate that the majority of BEAs compared skill and performance interventions on computational fluency; however, the methodology across the included studies varied. Strengths and limitations of the research, in addition to implications for research and practice, are discussed.

Download Full-text

A review of common rates of improvement when implementing whole-number operation math interventions.

School Psychology ◽

10.1037/spq0000360 ◽

2020 ◽

Vol 35 (5) ◽

pp. 353-362

Author(s):

Benjamin G. Solomon ◽

Brian C. Poncy ◽

Carmela Battista ◽

Kayla V. Campaña

Keyword(s):

Math Interventions ◽

Whole Number

Download Full-text

Toward a Causally Informative Fit Index of Longitudinal Models: A Within-Study Design Approach

10.31234/osf.io/5cbkt ◽

2021 ◽

Author(s):

Sirui Wan ◽

Timothy R. Brick ◽

Daniela Alvarez-Vargas ◽

Drew H Bailey

Keyword(s):

Study Design ◽

Structural Equation ◽

Design Approach ◽

Equation Modeling ◽

Fit Indices ◽

Longitudinal Models ◽

Math Interventions ◽

Model Fit Indices ◽

Fit Index

In structural equation modeling, plausible competing theories can imply similar or equivalent covariance matrices and thus show similar or identical model fit indices, despite making very different causal predictions. We propose a method for selecting among longitudinal models on the basis of causal information. We use a within-study design approach and present an index of causal fit for choosing among models on the basis of their fit with causally informative estimates, in cases in which research designs allow for strong causal estimates. We test for the usefulness and validity of the approach by applying it to data from three randomized controlled trials of early math interventions with longitudinal follow-up assessments. We find that, across datasets, some models consistently outperform other models at forecasting later experimental impacts, traditional fit indices are not strongly related to our index of causal fit, and models show consistent patterns of similarity and discrepancy between statistical fit and causal fit. A simulation study finds that when assumptions are met, the index of causal fit can recover the generating model at rates higher than those of statistical fit indices, and is less redundant with statistical fit indices than they are with each other. Results support the validity of our proposed approach and suggest that it can be useful for choosing among models.

Download Full-text

Special Education Math Interventions

10.18122/sped_facpubs/133/boisestate ◽

2021 ◽

Author(s):

Gena Nelson

Keyword(s):

Meta Analysis ◽

Quality Of Data ◽

Quality Of Reporting ◽

Eligibility Criteria ◽

Mathematics Intervention ◽

Screening Criteria ◽

Math Interventions ◽

Meta Analyses ◽

Analysis Quality

The purpose of document is to provide readers with the coding protocol that authors used to code 22 mathematics intervention meta-analyses focused on participants with or at-risk of disabilities. The author drafted this coding protocol based on the meta-analysis quality indicators recommended by Talbott et al. (2018, pp. 248–249); specifically, the author considered the variables presented in Table 1 of Talbott et al. and supplemented the information so that the variables and definitions were specific to the purpose of this systematic review. We coded each meta-analysis for 53 variables across eight categories, including: Quality of Clear Research Questions, Quality of Eligibility Criteria, Quality of Search Procedures, Quality of Screening Criteria, Quality of Coding Procedures, Quality of Research Participants and Contexts, Quality of Data Analysis Plan, and Quality of Reporting Results. The mean interrater reliability across all codes using this protocol was 87.8% (range across categories = 74% –100%).

Download Full-text