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The impact of computer-based tutorials on high school math proficiency

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The benefits of mathematical-related skills are well documented in the economics and education literature. Even in spite of such evidence, proficiency levels among US high school students remain persistently low. This is especially true for the State of Nevada. As a result, the Clark County School District (CCSD) made available to students a computer-aided math tutorial prior to taking the High School Proficiency Exam (HSPE) in mathematics. As such, we utilize a novel dataset and explore the impact of computer-aided learning on mathematics proficiency rates for 10th and 11th graders in the CCSD. Our results provide some evidence of increased proficiency in mathematics related to tutorial participation. This is especially true for minority students. However, causal claims are limited due to the inability to rule out a zero lower bound on the estimated average treatment effects.

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  1. Using district data for the 2009/2010 school year, O’Brien et al. (2011) find that a 49.6% math proficiency rate for 10th graders in Clark County lags behind three comparable peer districts: Broward County (73.0%), Houston ISD (68.0%), and Miami-Dade County (73.0%).

  2. See

  3. Unfortunately, we do not have scores for either the tutorial pre- or posttest. Note, these pre- and posttest scores from the tutorial are not the HSPE scores (our outcome variable of interest).

  4. The institutional settings of NCLB in Nevada have evolved over the years. Nevada submitted an initial plan called “All Children Can Succeed” in June 2002 (Nevada Department of Education 2002). At the onset, Nevada relied on the Terra Nova exam scores to define proficiency across various subjects, which was already in use for the statewide testing program. However, Nevada later implemented revised methods relying more on criterion-referenced exams to determine proficiency levels, hence the use of HSPE scores in mathematics (and other subjects) as a means of satisfying the upper-grade testing requirement in NCLB (Nevada Legislative Council Bureau 2005).

  5. We focus on the first attempt to keep the identification as clean as possible. In particular, we are concerned that with some students having taken the exam multiple times over a short period, outcomes can be impacted if learning has occurred with respect to material on the HSPE, the testing environment, etc. (see Matton et al. 2011). We further condition the exam scores on the first attempt not being a “retest” exam score. We chose not to include these “retest” students because it was unclear as to when they actually retested and what the environment was like in which they retested. However, we again conduct the analysis including these students, and the results are qualitatively similar.

  6. In applying the two methods, we use the Stata command -tebounds- for the nonparametric bounds approach of Kreider et al. (2012) and the Stata command -bmte- for the minimum biased estimator of Millimet and Tchernis (2013). See McCarthy et al. (2014) and McCarthy et al. (2015) for further details on the Stata commands.

  7. Like Kreider et al. (2012), we drop \(X\in \varOmega \) going forward for notational simplicity.

  8. Given the nature of the computerized tutorial, we precisely know who participated in the tutorial program since actual time spent in the program is digitally recorded. Thus, unlike Kreider et al. (2012) and Millimet and Roy (2015) who rely on self-reported participation, we do not allow for misclassification errors.

  9. See Proposition 1 in Manski and Pepper (2000).

  10. Steenbergen-Hu and Cooper (2013) review 26 different reports on intelligent tutoring systems spanning 1997 to 2010 and find that tutoring had at worse a nonnegative impact on achievement outcomes.

  11. As noted in McCarthy et al. (2015), combining the MTR assumption with the MTS–MIV assumptions is slightly more complicated since \(P[{\textit{MP}}(j)=1 ] ,\) \(j=0,1\) are bounded separately within each of the k cells. Since the MTR assumption requires that tutorial participation have no negative impact on the probability of being proficient in math, then it must be the case that \(B_{k}^{L}\) of \(P[{\textit{MP}}(1)=1] \) must be strictly less than \(B_{k}^{U}\) of \(P[{\textit{MP}}(0)=1] \) \(\forall \) \(k=1,\ldots ,K.\)

  12. Likelihood ratio tests for heteroskedasticity were conducted, and results are available upon request.

  13. All Imbens and Manski (2004) confidence intervals reported are at the 95% level.

  14. The covariates utilized in each model are indicators for minority status, gender, English proficient, refugee status, gifted and talented, and for being in the tenth grade. Note, the exogenous selection results presented in the previous section correspond to OLS when regressing math proficiency on only a treatment indicator.

  15. The cost of $5.00 per license comes from conversations with individuals at CCSD who recall the program during the 2006/2007 school year. The numbers provided to us were in the range of $4.00 to $5.00 per license. We opt to use the more aggressive $5.00 per license to construct an upper bound on the cost per additional proficient student.


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The authors have benefited from helpful suggestions from the co-editors, Wenhua Di and Daniel Millimet, and two anonymous referees, as well as participants at the Intent versus Impact: Evaluating Individual- and Community-Based Programs conference hosted by the Federal Reserve Bank of Dallas.

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Correspondence to Ian K. McDonough.

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McDonough, I.K., Tra, C.I. The impact of computer-based tutorials on high school math proficiency. Empir Econ 52, 1041–1063 (2017).

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