Educational research aims to expand our understanding of teaching and learning processes to improve student outcomes. A key part of research is critically evaluating existing studies to recognize both strengths that can inform practice and weaknesses that future inquiries can seek to remedy. This paper will critique a published research study on computer-assisted instruction in middle school mathematics classrooms to provide an example of this important process.
The study under review, “The Effects of Computer-Assisted Instruction on Mathematics Achievement in Sixth Grade Students” (Smith, 2019), investigated the impact of a computer program called MathMaster that provided supplemental math instruction to sixth grade students. The study aimed to determine if using MathMaster 15-20 minutes per week improved math test scores compared to traditional classroom-only instruction. A total of 210 sixth grade students from three suburban middle schools participated. Schools were randomly assigned to treatment (MathMaster) and control (no computer) conditions.
One strength of Smith’s (2019) study is the use of a randomized controlled trial (RCT) design, which is considered the gold standard for establishing a causal relationship between an intervention and outcomes. RCTs reduce threats to internal validity such as selection bias by ensuring comparable treatment and control groups through random assignment. In this case, randomly assigning intact classrooms to conditions helped control for potential baseline differences between students that could otherwise confound results.
Another strength is the large sample size of 210 students, increasing statistical power to detect effects if present. Having students clustered within classrooms and schools also enhances external validity by better approximating real-world conditions compared to studying individuals independently. Measurement of the primary dependent variable, standardized math test scores, also lends objective comparability across conditions.
Some limitations reduce the strength of causal conclusions that can be drawn from Smith’s (2019) study. First, lacking pretest math scores means prior achievement differences between conditions cannot be ruled out as alternative explanations for any posttest differences observed. Second, while random assignment occurred at the classroom level, there is no record of whether treatment fidelity was maintained—whether control classes truly received no computer instruction. Breaches in implementation integrity threaten internal validity.
Third, the study did not report basic demographic characteristics of participating students and schools. Without this participant description, generalizability of any findings is uncertain, particularly regarding diverse student populations. Fourth, the short 15-20 minute weekly dosage of the MathMaster intervention may not have provided sufficient instructional time and repetition to reasonably expect large achievement effects. A longer or more intensive intervention could yield different results.
Finally, the study only measured outcomes immediately post-intervention, without a follow-up assessment later in the school year or the following year. Short-term gains may not persist or translate to long-term learning retention and application. Measuring maintenance and generalization of skills over additional time periods would strengthen conclusions about the program’s effectiveness and inform decisions about sustained use.
While Smith’s (2019) study employed a rigorous RCT design and measured an important educational outcome, several limitations temper the strength of conclusions that can be drawn from its results regarding the efficacy of the MathMaster program. Specifically, lacking pretest data and details about implementation fidelity, participant and school characteristics, a brief dosage, and longer-term follow up prevent firm determination about whether observed differences were caused by the intervention versus other plausible factors. Future research addressing these shortcomings could provide more definitive evidence of computer-assisted instruction’s benefits for mathematics learning. By critically examining both strengths and weaknesses, this type of research critique aims to advance educational practices through improved research methods over time.