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Washington State Institute for Public Policy
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Tutoring: By adults, one-on-one, structured

Pre-K to 12 Education
Benefit-cost methods last updated December 2019.  Literature review updated May 2020.
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The tutoring programs included in this analysis provide one-on-one tutoring to struggling students in English language arts and/or mathematics. The evaluated programs typically allow tutors to exercise discretion when selecting and implementing tutoring strategies. The programs typically serve early elementary school students and provide, on average, 35 hours of tutoring time to an individual student over nine months. The tutors are non-certificated adults (e.g. paraeducators and community volunteers) who receive approximately three hours of training.
 
ALL
BENEFIT-COST
META-ANALYSIS
CITATIONS
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2018). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $3,611 Benefits minus costs $12,530
Participants $8,482 Benefit to cost ratio $5.66
Others $4,474 Chance the program will produce
Indirect ($1,346) benefits greater than the costs 92 %
Total benefits $15,221
Net program cost ($2,691)
Benefits minus cost $12,530

^WSIPP’s benefit-cost model does not monetize this outcome.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment age No. of effect sizes Treatment N Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
6 1 29 0.090 0.326 6 n/a n/a n/a 0.090 0.783
6 30 3916 0.244 0.041 6 0.098 0.045 17 0.399 0.001
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Test scores Labor market earnings associated with test scores $3,611 $8,482 $4,474 $0 $16,567
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($1,346) ($1,346)
Totals $3,611 $8,482 $4,474 ($1,346) $15,221
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Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $2,654 2018 Present value of net program costs (in 2018 dollars) ($2,691)
Comparison costs $0 2018 Cost range (+ or -) 20 %
In the evaluations included in this analysis, the average non-structured one-on-one tutoring program provides 35 hours of tutoring per student and three hours of training time per tutor. To calculate a per-student annual cost, we use average Washington State compensation costs (including benefits) for K-12 staff (i.e., elementary teachers and paraeducators) as reported by the Office of the Superintendent of Public Instruction for the 2018-19 school year and weight by the treatment samples in each study.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in discounted dollars. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Citations Used in the Meta-Analysis

Allor, J., & McCathren, R. (2004). The efficacy of an early literacy tutoring program implemented by college students. Learning Disabilities Research and Practice, 19(2), 116-129.

Fives, A., Kearns, N., Devaney, C., Canavan, J., Russell, D., Lyons, R., . . . O'Brien, A. (2013). A one-to-one programme for at-risk readers delivered by older adult volunteers. Review of Education, 1(3), 254-280.

Fuchs, L.S., Geary, D.C., Compton, D.L., Fuchs, D., Schatschneider, C., Hamlett, C. L., . . . Changas, P. (2013). Effects of first-grade number knowledge tutoring with contrasting forms of practice. Journal of Educational Psychology, 105(1), 58-77.

Jacob, R.T., Smith, T.J., Willard, J.A., & Rifkin, R.E. (2014). Reading Partners: The implementation and effectiveness of a one-on-one tutoring program delivered by community volunteers (MDRC Policy Brief). New York: MDRC.

Jenkins, J., Peyton, J., Sanders, E., & Vadasy, P. (2004). Effects of reading decodable texts in supplemental first-grade tutoring. Scientific Studies of Reading, 8(1) 53-85.

Lee, Y.S., Morrow-Howell, N., Jonson-Reid, M., & McCrary, S. (2010). The effect of the Experience Corps® Program on student reading outcomes. Education and Urban Society, 44(1) 97-118.

May, H., Gray, A., Gillespie, J., Sirinides, P., Sam, C., Goldsworthy, H., . . . Tognatta, N. (2013). Evaluation of the i3 scale-up of reading recovery: Year one report, 2011-12. Consortium for Policy Research in Education.

May, H., Goldsworthy, H., Armijo, M., Gray, A., Sirinides, P., & Blalock, T. (2014). Evaluation of the i3 scale-up of reading recovery. Year two report, 2012-13. UPenn. Consortium for Policy Research in Education.

Mayfield, L.G. (2000). The effects of structured one-on-one tutoring in sight word recognition of first-grade students at-risk for reading failure. Dissertation Abstracts International, 61(02), 481A.

Morris, D., Shaw, B., & Perney, J. (1990). Helping low readers in grades 2 and 3: An after-school volunteer tutoring program. Elementary School Journal, 91(2), 133-150.

Mostow, J., Aist, G., Burkhead, P., Corbett, A., Cuneo, A., Eitelman, S., . . . Tobin, B. (2003). Evaluation of an automated reading tutor that listens: Comparison to human tutoring and classroom instruction. Journal of Educational Computing Research, 29(1), 61-117.

Nielson, B.B. (1992). Effects of parent and volunteer tutoring on reading achievement of third grade at-risk students. Dissertation Abstracts International, 52(10), 3570A.

Pinnell, G.S., Lyons, C.A., DeFord, D.E., Bryk, A.S., & Seltzer, M. (1994). Comparing instructional models for the literacy education of high-risk first graders. Reading Research Quarterly, 29(1), 9-39.

Pullen, P.C., Lane, H.B., & Monaghan, M.C. (2004). Effects of a volunteer tutoring model on the early literacy development of struggling first grade students. Reading Research and Instruction, 43(4), 21-40.

Rodick, J.D., & Henggeler, S.W. (1980). The short-term and long-term amelioration of academic and motivational deficiencies among low-achieving inner-city adolescents. Child Development, 51(4), 1126-1132.

Rutt, S., Easton, C., & Oliver, S. (2014). Catch up numeracy: Evaluation report and executive summary. National Foundation for Educational Research.

Schwartz, R.M. (2005). Literacy learning of at-risk first-grade students in the reading recovery early intervention. Journal of Educational Psychology, 97(2), 257-267.

Smith, T.M., Cobb, P., Farran, D.C., Cordray, D.S., & Munter, C. (2013). Evaluating math recovery: Assessing the causal impact of a diagnostic tutoring program on student achievement. American Educational Research Journal, 50(2), 397-428.

Vadasy, P.F., & Sanders, E.A. (2008). Code-oriented instruction for kindergarten students at risk for reading difficulties: a replication and comparison of instructional groupings. Reading and Writing, 21(9) 929-963.

Vadasy, P.F., & Sanders, E.A. (2011). Efficacy of supplemental phonics-based instruction for low-skilled first graders: How language minority status and pretest characteristics moderate treatment response. Scientific Studies of Reading, 15(6) 471-497.

Vadasy, P.F., Jenkins, J.R., Antil, L.R., Wayne, S.K., & O'Connor, R.E. (1997). The effectiveness of one-to-one tutoring by community tutors for at-risk beginning readers. Learning Disability Quarterly, 20(2), 126-139.

Vadasy, P.F., Jenkins, J.R., & Pool, K. (2000). Effects of tutoring in phonological and early reading skills on students at risk for reading disabilities. Journal of Learning Disabilities, 33(6), 579-590.

Vadasy, P.F., Sanders, E.A., & Tudor, S. (2007). Effectiveness of paraeducator-supplemented individual instruction: Beyond basic decoding skills. Journal of Learning Disabilities, 40(6), 508-525.

Vadasy, P.F. & Sanders, E.A. (2010). Efficacy of supplemental phonics-based instruction for low-skilled kindergarteners in the context of language minority status and classroom phonics instruction. Journal of Educational Psychology, 102(4) 786.

Vadasy, P.F., Sanders, E.A., & Peyton, J.A. (2006). Code-oriented instruction for kindergarten students at risk for reading difficulties: A randomized field trial with paraeducator implementers. Journal of Educational Psychology, 98, 508-528.