blog posts and news stories

U.S. Department of Education Could Expand its Concept of Student Growth

The continuing debate about the use of student test scores as a part of teacher evaluation misses an essential point. A teacher’s influence on a student’s achievement does not end in spring when the student takes the state test (or is evaluated using any of the Student Learning Objectives methods). An inspiring teacher, or one that makes a student feel recognized, or one that digs a bit deeper into the subject matter, may be part of the reason that the student later graduates high school, gets into college, or pursues a STEM career. These are “student achievements,” but they are ones that show up years after a teacher had the student in her class. As a teacher is getting students to grapple with a new concept, the students may not demonstrate improvements on standardized tests that year. But the “value-added” by the teacher may show up in later years.

States and districts implementing educator evaluations as part of their NCLB waivers are very aware of the requirement that they must “use multiple valid measures in determining performance levels, including as a significant factor data on student growth …” Student growth is defined as change between points in time in achievement on assessments. Student growth defined in this way obscures a teacher’s contribution to a student’s later school career.

As a practical matter, it may seem obvious that for this year’s evaluation, we can’t use something that happens next year. But recent analyses of longitudinal data, reviewed in an excellent piece by Raudenbush show that it is possible to identify predictors of later student achievement associated with individual teacher practices and effectiveness. The widespread implementation of multiple-measure teacher evaluations is starting to accumulate just the longitudinal datasets needed to do these predictive analyses. On the basis of these analyses we may be able to validate many of the facets of teaching that we have found, in analyses of the MET data, to be unrelated to student growth as defined in the waiver requirements.

Insofar as we can identify, through classroom observations and surveys, practices and dispositions that are predictive of later student achievement such as college going, then we have validated those practices. Ultimately, we may be able to substitute classroom observations and surveys of students, peers, and parents for value-added modeling based on state tests and other ad hoc measures of student growth. We are not yet at that point, but the first step will be to recognize that a teacher’s influence on a student’s growth extends beyond the year she has the student in the class.

Does 1 teacher = 1 number? Some Questions About the Research on Composite Measures of Teacher Effectiveness

We are all familiar with approaches to combining student growth metrics and other measures to generate a single measure that can be used to rate teachers for the purpose of personnel decisions. For example, as an alternative to using seniority as the basis for reducing the workforce, a school system may want to base such decisions—at least in part—on a ranking based on a number of measures of teacher effectiveness. One of the reports released January 8 by the Measures of Effective Teaching (MET) addressed approaches to creating a composite (i.e., a single number that averages various aspects of teacher performance) from multiple measures such as value-added modeling (VAM) scores, student surveys, and classroom observations. Working with the thousands of data points in the MET longitudinal database, the researchers were able to try out multiple statistical approaches to combining measures. The important recommendation from this research for practitioners is that, while there is no single best way to weight the various measures that are combined in the composite, balancing the weights more evenly tends to increase reliability.

While acknowledging the value of these analyses, we want to take a step back in this commentary. Here we ask whether agencies may sometimes be jumping to the conclusion that a composite is necessary when the individual measures (and even the components of these measures) may have greater utility than the composite for many purposes.

The basic premise behind creating a composite measure is the idea that there is an underlying characteristic that the composite can more or less accurately reflect. The criterion for a good composite is the extent to which the result accurately identifies a stable characteristic of the teacher’s effectiveness.

A problem with this basic premise is that in focusing on the common factor, the aspects of each measure that are unrelated to the common factor get left out—treated as noise in the statistical equation. But, what if observations and student surveys measure things that are unrelated to what the teacher’s students are able to achieve in a single year under her tutelage (the basis for a VAM score)? What if there are distinct domains of teacher expertise that have little relation to VAM scores? By definition, the multifaceted nature of teaching gets reduced to a single value in the composite.

This single value does have a use in decisions that require an unequivocal ranking of teachers, such as some personnel decisions. For most purposes, however, a multifaceted set of measures would be more useful. The single measure has little value for directing professional development, whereas the detailed output of the observation protocols are designed for just that. Consider a principal deciding which teachers to assign as mentors, or a district administrator deciding which teachers to move toward a principalship. Might it be useful, in such cases, to have several characteristics to represent different dimensions of abilities relevant to success in the particular roles?

Instead of collapsing the multitude of data points from achievement, surveys, and observations, consider an approach that makes maximum use of the data points to identify several distinct characteristics. In the usual method for constructing a composite (and in the MET research), the results for each measure (e.g., the survey or observation protocol) are first collapsed into a single number, and then these values are combined into the composite. This approach already obscures a large amount of information. The Tripod student survey provides scores on the seven Cs; an observation framework may have a dozen characteristics; and even VAM scores, usually thought of as a summary number, can be broken down (with some statistical limitations) into success with low-scoring vs. with high-scoring students (or any other demographic category of interest). Analyzing dozens of these data points for each teacher can potentially identify several distinct facets of a teacher’s overall ability. Not all facets will be strongly correlated with VAM scores but may be related to the teacher’s ability to inspire students in subsequent years to take more challenging courses, stay in school, and engage parents in ways that show up years later.

Creating a single composite measure of teaching has value for a range of administrative decisions. However, the mass of teacher data now being collected are only beginning to be tapped for improving teaching and developing schools as learning organizations.

Final Report Released on The Efficacy of PCI’s Reading Program

Empirical has released the final report of a three-year longitudinal study on the efficacy of the PCI Reading Program, which can be found on our reports page. This study, the first formal assessment of the PCI Reading Program, evaluated the program among a sample of third- through eighth-grade students with supported-level disabilities in Florida’s Brevard Public Schools and Miami-Dade County Public Schools. The primary goal of the study was to identify whether the program could achieve its intended purpose of teaching specific sight words. The study was completed in three “phases,” or school years. The results from Phase 1 and 2 showed a significant positive effect on student sight word achievement and Phase 2 supported the initial expectation that two years of growth would be greater than one year (read more on results of Phase 1 and Phase 2).

“Working with Empirical Education was a win for us on many fronts. Their research was of the highest quality and has really helped us communicate with our customers through their several reports and conference presentations. They went beyond just outcomes to show how teachers put our reading program to use in classrooms. In all their dealings with PCI and with the school systems they were highly professional and we look forward to future research partnership opportunities.” - Lee Wilson, President & CEO, PCI Educational Publishing

In Phase 3, the remaining sample of students was too small to conduct any impact analyses, so researchers investigated patterns in students’ progress through the program. The general findings were positive in that the exploration confirmed that students continue to learn more sight words with a second year of exposure to PCI although at a slower pace than expected by the developers. Furthermore, findings across all three phases show high levels of teacher satisfaction with the program. Along with this positive outcome, teacher-reported student engagement levels were also high.

New RFP calls for Building Regional Research Capacity

The US Department of Education (ED) has just released the eagerly anticipated RFP for the next round of the Regional Education Laboratories (RELs). This RFP contains some very interesting departures from how the RELs have been working, which may be of interest especially to state and local educators.

For those unfamiliar with federal government organizations, the RELs are part of the National Center for Education Evaluation and Regional Assistance (abbreviated NCEE), which is within the Institute of Education Sciences (IES), part of ED. The country is divided up into ten regions, each one served by a REL—so the RFP announced today is really a call for proposals in ten different competitions. The RELs have been in existence for decades but their mission has evolved over time. For example, the previous RFP (about 6 years ago) put a strong emphasis on rigorous research, particularly randomized control trials (RCTs) leading the contractors in each of the 10 regions to greatly expand their capacity, in part by bringing in subcontractors with the requisite technical skills. (Empirical conducted or assisted with RCTs in four of the 10 regions.) The new RFP changes the focus in two essential ways.

First, one of the major tasks is building capacity for research among practitioners. Educators at the state and local levels told ED that they needed more capacity to make use of the longitudinal data systems that the ED has invested in through grants to the states. It is one thing to build the data systems. It is another thing to use the data to generate evidence that can inform decisions about policies and programs. Last month at the conference of the Society for Research on Educational Effectiveness, Rebecca Maynard, Commissioner of NCEE talked about building a “culture of experimentation” among practitioners and building their capacity for simpler experiments that don’t take so long and are not as expensive as those NCEE has typically contracted for. Her point was that the resulting evidence is more likely to be used if the practitioners are “up close and immediate.”

The second idea found in the RFP for the RELs is that each regional lab should work through “alliances” of state and local agencies. These alliances would cross state boundaries (at least within the region) and would provide an important part of the REL’s research agenda. The idea goes beyond having an advisory panel for the REL that requests answers to questions. The alliances are also expected to build their own capacity to answer these questions using rigorous research methods but applying them cost-effectively and opportunistically. The capacity of the alliances should outlive the support provided by the RELs. If your organization is part of an existing alliance and would like to get better at using and conducting research, there are teams being formed to go after the REL contracts that would be happy to hear from you. (If you’re not sure who to call, let us know and we’ll put you in touch with an appropriate team.)

2010-2011: The Year of the VAM

If you haven’t heard about Value-Added Modeling (VAM) in relation to the controversial teacher ratings in Los Angeles and subsequent brouhaha in the world of education, chances are that you’ll hear about it in the coming year.

VAM is a family of statistical techniques for estimating the contribution of a teacher or of a school to the academic growth of students. Recently, the LA Times obtained the longitudinal test score records for all the elementary school teachers and students in LA Unified and had a RAND economist (working as an independent consultant) run the calculations. The result was a “score” for all LAUSD elementary school teachers.

Reactions to the idea that a teacher could be evaluated using a set of test scores—in this case from the California Standards Test—were swift and divisive. The concept was denounced by the teachers’ union, with the local leader calling for a boycott. Meanwhile, the US Secretary of Education, Arne Duncan, made headlines by commenting favorably on the idea. The LA Times quotes him as saying “What’s there to hide? In education, we’ve been scared to talk about success.”

There is a tangle of issues here, along with exaggerations, misunderstandings, and confusion between research techniques and policy decisions. This column will address some of the issues over the coming year. We also plan to announce some of our own contributions to the VAM field in the form of project news.

The major hot-button issues include appropriate usage (e.g., for part or all of the input to merit pay decisions) and technical failings (e.g., biases in the calculations). Of course, these two issues are often linked; for example, many argue that biases may make VAM unfair for individual merit pay. The recent Brief from the Economic Policy Institute, authored by an impressive team of researchers (several our friends/mentors from neighboring Stanford), makes a well reasoned case for not using VAM as the only input to high-stakes decisions. While their arguments are persuasive with respect to VAM as the lone criterion for awarding merit pay or firing individual teachers, we still see a broad range of uses for the technique, along with the considerable challenges.

For today, let’s look at one issue that we find particularly interesting: How to handle teacher collaboration in a VAM framework. In a recent Education Week commentary, Kim Marshall argues that any use of test scores for merit pay is a losing proposition. One of the many reasons he cites is its potentially negative impact on collaboration.

A problem with an exercise like that conducted by the LA Times is that there are organizational arrangements that do not come into the calculations. For example, we find that team teaching within a grade at a school is very common. A teacher with an aptitude for teaching math may take another teacher’s students for a math period, while sending her own kids to the other teacher for reading. These informal arrangements are not part of the official school district roster. They can be recorded (with some effort) during the current year but are lost for prior years. Mentoring is a similar situation, wherein the value provided to the kids is distributed among members of their team of teachers. We don’t know how much difference collaborative or mentoring arrangements make to individual VAM scores, but one fear in using VAM in setting teacher salaries is that it will militate against productive collaborations and reduce overall achievement.

Some argue that, because VAM calculations do not properly measure or include important elements, VAM should be disqualified from playing any role in evaluation. We would argue that, although they are imperfect, VAM calculations can still be used as a component of an evaluation process. Moreover, continued improvements can be made in testing, in professional development, and in the VAM calculations themselves. In the case of collaboration, what is needed are ways that a principal can record and evaluate the collaborations and mentoring so that the information can be worked into the overall evaluation and even into the VAM calculation. In such an instance, it would be the principal at the school, not an administrator at the district central office, who can make the most productive use of the VAM calculations. With knowledge of the local conditions and potential for bias, the building leader may be in the best position to make personnel decisions.

VAM can also be an important research tool—using consistently high and/or low scores as a guide for observing classroom practices that are likely to be worth promoting through professional development or program implementations. We’ve seen VAM used this way, for example, by the research team at Wake County Public Schools in North Carolina in identifying strong and weak practices in several content areas. This is clearly a rich area for continued research.

The LA Times has helped to catapult the issue of VAM onto the national radar. It has also sparked a discussion of how school data can be used to support local decisions, which can’t be a bad thing.

Report Released on Phase Two of The Efficacy of PCI’s Reading Program

The results are in for Phase Two of a five year longitudinal efficacy trial of PCI’s Reading Program for students with moderate to severe disabilities. This research builds upon an initial randomized control trial conducted last year that found that students in the PCI program had substantial success in learning sight words in comparison to students in the control group. Phase Two continues research in the Brevard and Miami–Dade County school districts with teachers of supported-level students in grades 3-8. Using both quasi-experimental and extra-experimental methods, findings again demonstrate that students who received PCI for two years achieved significantly higher scores on the sight word assessment than students who were not exposed to the program. However, student progress through the program was slower than initially expected by the developers. Empirical will continue to collect, integrate, and analyze outcomes for three more years.

The methodological designs for this study were presented at this year’s annual SREE conference in Washington, D.C. Results for this study will also be presented at the 2010 Annual AERA Meeting in Denver, CO. Meet the research team as they describe the study in further detail during the Division C poster session on May 3.

Webinar: Uncovering ARRA’s Research Requirements

Researchers at Empirical Education provided a detailed overview of the various research themes and requirements of the ARRA stimulus initiatives with state department of education officials during their December 9 webinar entitled, “Meet Stimulus Funds’ Research Requirements with Confidence.“ The webinar gave specific examples of how states may start planning their applications and building research partnerships, as well as an overview of the ED’s current thinking about building local research capacity. The initiatives that were discussed included Race to the Top, Enhancing Education Through Technology, Investing in Innovation, Title I School Improvement Grants, and State Longitudinal Data Systems.

A follow-up webinar was broadcasted on January 20, 2010; it outlined a specific example of a program evaluation design that districts can use with existing data. Stay tuned for future webinar topics on more alternative experimental research designs.

Uncovering ARRA’s Research Requirements

Researchers at Empirical Education provided a detailed overview of the various research themes and requirements of the ARRA stimulus initiatives with state department of education officials during their December 9th webinar entitled, “Meet Stimulus Funds’ Research Requirements with Confidence.” The webinar gave specific examples of how states may start planning their applications, building research partnerships, as well as an overview of the ED’s current thinking about building local research capacity. The initiatives that were discussed included Race to the Top, Enhancing Education Through Technology, Investing in Innovation, Title I School Improvement Grants, and State Longitudinal Data Systems.

i3 Request for Proposals Calls for New Approaches to Rigorous Evaluation

In the strongest indication yet that the new administration is serious about learning from its multi-billion-dollar experience, the draft notice for the Invest in Innovation (i3) grants sets out new requirements for research and evaluation. While it is not surprising that the U.S. Department of Education requires scientific evidence for programs asking for funds for expansion and scaling up, it is important to note that strong evidence is now being defined not just in terms of rigorous methods but also in terms of “studies that in total include enough of the range of participants and settings to support scaling up to the State, regional, or national level.” This requirement for generalizability is a major step toward sponsoring research that has value for practical decisions. Along the same lines, high quality evaluations are those that include implementation data and performance feedback.

The draft notice also includes recognition of an important research design: “interrupted time series.” While not acceptable under the current What Works Clearinghouse criteria, this method—essentially looking for a change in a series of measures taken before and after implementing a new program—has enormous practical application for schools systems with solid longitudinal data systems.

Finally, we notice that ED is requiring that all evaluators cooperate with broader national efforts to combine evidence from multiple sources and will provide technical assistance to evaluators to assure consistency among researchers. They want to be sure at the end of the process they have useful evidence about what worked, what didn’t, and why.