This is the last of a four-part blog posting about changes needed to the legacy of NCLB to make research more useful to school decision makers. Here we show how lots of small studies can give better evidence to resolve achievement gaps. To read the the first 3 parts, use these links.
The NCLB-era of the single big study should be giving way to the analysis of the differential impacts for subgroups from multiple studies. This is the information that schools need in order to reduce achievement gaps. Today’s technology landscape is ready for this major shift in the research paradigm. The school shutdowns resulting from the COVID-19 pandemic have demonstrated that the value of edtech products goes beyond just the cost reduction of eliminating expensive print materials. Over the last decade digital learning products have collected usage data which provides rich and systematic evidence of how products are being used and by whom. At the same time, schools have accumulated huge databases of digital records on demographics and achievement history, with public data at a granularity down to the grade-level. Using today’s “big data” analytics, this wealth of information can be put to work for a radical reduction in the cost of showing efficacy.
Fast turnaround, low cost research studies will enable hundreds of studies to be conducted providing information to school decision-makers that answer their questions. Their questions are not just “which program, on average, produces the largest effect?” Their questions are “which program is most likely to work in my district, with my kids and teachers, and with my available resources, and which are most likely to reduce gaps of greatest concern?”
Meta-analysis is a method for combining multiple studies to increase generalizability (Shadish, Cook, & Campbell, 2002). With meta-analysis, we can test for stability of effects across sites and synthesize those results, where warranted, based on specific statistical criteria. While moderator analysis is considered merely exploratory in the NCLB-era, using meta-analysis, moderator results from multiple small studies, can in combination provide confirmation of a differential impact. Meta-analysis, or other approaches to research synthesis, combined with big data present new opportunities to move beyond the NCLB-era philosophy that prizes the single big study to prove the efficacy of a program.
While addressing WWC and ESSA standards, we caution, that a single study in one school district, or even several studies in several school districts, may not provide enough useful information to generalize to other school districts. For research to be the most effective, we need studies in enough districts to represent the full diversity of relevant populations. Studies need to systematically include moderator analysis for an effective way to generalize impact for subgroups.
The definitions provided in ESSA do not address how much information is needed to generalize from a particular study for implementation in other school districts. While we accept that well-designed Tier 2 or 3 studies are necessary to establish an appropriate level of rigor, we do not believe a single study is sufficient to declare a program will be effective across varied populations. We note that the Standards for Excellence in Education Research (SEER) recently adopted by the IES, call for facilitating generalizability.
After almost two decades of exclusive focus on the design of the single study we need to more effectively address achievement gaps with the specifics that school decision-makers need. Lowering the cost and turn-around time for research studies that break out subgroup results is entirely feasible. With enough studies qualified for meta-analysis, a new wealth of information will be available to educators who want to select the products that will best serve their students. This new order will democratize learning across the country, reducing inequities and raising student achievement in K-12 schools.