blog posts and news stories

Arkansas Implements Observation Engine Statewide

BloomBoard’s observation tool, EdReflect, has been used across the state of Arkansas since fall 2014. Last year, the Arkansas Department of Education piloted Observation Engine, an online observation training and calibration tool from Empirical Education Inc., in four districts under the state’s Equitable Access Plan. Accessible through the BloomBoard platform, Observation Engine allows administrators and other teacher evaluators to improve scoring calibration and reliability through viewing and rating videos of classroom lessons collected in thousands of classrooms across the country.

Paired with BloomBoard resources and training, the results were impressive. In one district, the number of observers scoring above target increased from 43% to 100%. Not only that but the percent discrepancy (scores that were two levels above or below the target) decreased from 9% to 0%. Similar results were found in the other three pilot districts, prompting decision makers to make Observation Engine readily available to districts throughout the state.

“EdReflect has proven to be a valuable platform for educator observations in Arkansas. The professional conversation, which results from the ability to provide timely feedback and shared understanding of effective practice, has proven to ensure a transparency and collaboration that we have not experienced before. With the addition of Empirical Education’s Observation Engine, credentialed teacher observers have ready access to increase inter-rater reliability and personal skill. For the first time this year, BloomBoard Collections and micro-credentials have begun meeting individualized professional learning needs for educators all over the state.”
– Sandra Hurst, Arkansas Department of Education

In July, the Arkansas Department of Education decided to offer Observation Engine to the entire state. About half of all districts in the state opted in to receive the service, with the implementation spanning three groups of users in Arkansas. The Beginning Administrators group has already started pursuing a micro-credential based on Observation Engine. Micro-credentials are a digital form of certification that indicate a person has demonstrated competency in a specific skill set. The Beginning Administrators group can earn their “Observation Skills for Beginning Administrators” micro-credential by demonstrating observation skill competencies using Observation Engine’s online observer calibration tool to practice and assess observation skills.

Next month, the 26 more districts under the Equitable Access Plan and the remaining Arkansas districts will begin using Observation Engine. We look forward to following and reporting on the progress of these districts during the 2016-17 school year.

2016-11-02

Feds Moving Toward a More Rational and Flexible Approach to Teacher Support and Evaluation

Congress is finally making progress on a bill to replace NCLB. Here’s an excerpt from a summary of the draft law.

TITLE II–
Helps states support teachers– The bill provides resources to states and school districts to implement various activities to support teachers, principals, and other educators, including allowable uses of funds for high quality induction programs for new teachers, ongoing professional development opportunities for teachers, and programs to recruit new educators to the profession. Ends federal mandates on evaluations, allows states to innovate- The bill allows, but does not require, states to develop and implement teacher evaluation systems. This bill eliminates the definition of a highly qualified teacher—which has proven onerous to states and school districts—and provides states with the opportunity to define this term.

This is very positive. It makes teacher evaluation no longer an Obama-imposed requirement but allows states, that want to do it (and there are quite a few of those), to use federal funds to support it. It removes the irrational requirement that “student growth” be a major component of these systems. This will lower the reflexive resistance from unions because the purpose of evaluation can be more clearly associated with teacher support (for more on that argument, see the Real Clear Education piece). It will also encourage the use of observation and feedback from administrators and mentors. Removing the outmoded definition of “highly qualified teacher” opens up the possibility of wider use of research-based analyses of what is important to measure in effective teaching.

A summary is also provided by EdWeek. On a separate note, it says: “That new research and innovation program that some folks were describing as sort of a next generation ‘Investing in Innovation’ program made it into the bill. (Sens. Orrin Hatch, R-Utah, and Michael Bennet, D-Colo., are big fans, as is the administration.)”

2015-11-24

Unintended Consequences of Using Student Test Scores to Evaluate Teachers

There has been a powerful misconception driving policy in education. It’s a case where theory was inappropriately applied to practice. The misconception has had unintended consequences. It is helping to lead large numbers of parents to opt out of testing and could very well weaken the case in Congress for accountability as ESEA is reauthorized.

The idea that we can use student test scores as one of the measures in evaluating teachers came into vogue with Race to the Top. As a result of that and related federal policies, 38 states now include measures of student growth in teacher evaluations.

This was a conceptual advance over the NCLB definition of teacher quality in terms of preparation and experience. The focus on test scores was also a brilliant political move. The simple qualification for funding from Race to the Top—a linkage between teacher and student data—moved state legislatures to adopt policies calling for more rigorous teacher evaluations even without funding states to implement the policies. The simplicity of pointing to student achievement as the benchmark for evaluating teachers seemed incontrovertible.

It also had a scientific pedigree. Solid work had been accomplished by economists developing value-added modeling (VAM) to estimate a teacher’s contribution to student achievement. Hanushek et al.’s analysis is often cited as the basis for the now widely accepted view that teachers make the single largest contribution to student growth. The Bill and Melinda Gates Foundation invested heavily in its Measures of Effective Teaching (MET) project, which put the econometric calculation of teachers’ contribution to student achievement at the center of multiple measures.

The academic debates around VAM remain intense concerning the most productive statistical specification and evidence for causal inferences. Perhaps the most exciting area of research is in analyses of longitudinal datasets showing that students who have teachers with high VAM scores continue to benefit even into adulthood and career—not so much in their test scores as in their higher earnings, lower likelihood of having children as teenagers, and other results. With so much solid scientific work going on, what is the problem with applying theory to practice? While work on VAMs has provided important findings and productive research techniques, there are four important problems in applying these scientifically-based techniques to teacher evaluation.

First, and this is the thing that should have been obvious from the start, most teachers teach in grades or subjects where no standardized tests are given. If you’re conducting research, there is a wealth of data for math and reading in grades three through eight. However, if you’re a middle-school principal and there are standardized tests for only 20% of your teachers, you will have a problem using test scores for evaluation.

Nevertheless, federal policy required states—in order to receive a waiver from some of the requirements of NCLB—to institute teacher evaluation systems that use student growth as a major factor. To fill the gap in test scores, a few districts purchased or developed tests for every subject taught. A more wide-spread practice is the use of Student Learning Objectives (SLOs). Unfortunately, while they may provide an excellent process for reflection and goal setting between the principal and teacher, they lack the psychometric properties of VAMs, which allow administrators to objectively rank a teacher in relation to other teachers in the district. As the Mathematica team observed, “SLOs are designed to vary not only by grade and subject but also across teachers within a grade and subject.” By contrast, academic research on VAM gave educators and policy makers the impression that a single measure of student growth could be used for teacher evaluation across grades and subjects. It was a misconception unfortunately promoted by many VAM researchers who may have been unaware that the technique could only be applied to a small portion of teachers.

There are several additional reasons that test scores are not useful for teacher evaluation.

The second reason is that VAMs or other measures of student growth don’t provide any indication as to how a teacher can improve. If the purpose of teacher evaluation is to inform personnel decisions such as terminations, salary increases, or bonuses, then, at least for reading and math teachers, VAM scores would be useful. But we are seeing a widespread orientation toward using evaluations to inform professional development. Other kinds of measures, most obviously classroom observations conducted by a mentor or administrator—combined with feedback and guidance—provide a more direct mapping to where the teacher needs to improve. The observer-teacher interactions within an established framework also provide an appropriate managerial discretion in translating the evaluation into personnel decisions. Observation frameworks not only break the observation into specific aspects of practice but provide a rubric for scoring in four or five defined levels. A teacher can view the training materials used to calibrate evaluators to see what the next level looks like. VAM scores are opaque in contrast.

Third, test scores are associated with a narrow range of classroom practice. My colleague, Val Lazarev, and I found an interesting result from a factor analysis of the data collected in the MET project. MET collected classroom videos from thousands of teachers, which were then coded using a number of frameworks. The students were tested in reading and/or math using an assessment that was more focused on problem-solving and constructive items than is found in the usual state test. Our analysis showed that a teacher’s VAM score is more closely associated with the framework elements related to classroom and behavior management (i.e., keeping order in the classroom) than the more refined aspects of dialog with students. Keeping the classroom under control is a fundamental ability associated with good teaching but does not completely encompass what evaluators are looking for. Test scores, as the benchmark measure for effective teaching, may not be capturing many important elements.

Fourth, achievement test scores (and associated VAMs) are calculated based on what teachers can accomplish with respect to improving test scores from the time students appear in their classes in the fall to when they take the standardized test in the spring. If you ask people about their most influential teacher, they talk about being inspired to take up a particular career or about keeping them in school. These are results that are revealed in following years or even decades. A teacher who gets a student to start seeing math in a new way may not get immediate results on the spring test but may get the student to enroll in a more challenging course the next year. A teacher who makes a student feel at home in class may be an important part of the student not dropping out two years later. Whether or not teachers can cause these results is speculative. But the characteristics of warm, engaging, and inspiring teaching can be observed. We now have analytic tools and longitudinal datasets that can begin to reveal the association between being in a teacher’s class and the probability of a student graduating, getting into college, and pursuing a productive career. With records of systematic classroom observations, we may be able, in the future, to associate teaching practices with benchmarks that are more meaningful than the spring test score.

The policy-makers’ dream of an algorithm for translating test scores into teacher salary levels is a fallacy. Even the weaker provisions such as the vague requirement that student growth must be an important element among multiple measures in teacher evaluations has led to a profusion of methods of questionable utility for setting individual goals for teachers. But the insistence on using annual student achievement as the benchmark has led to more serious, perhaps unintended, consequences.

Teacher unions have had good reason to object to using test scores for evaluations. Teacher opposition to this misuse of test scores has reinforced a negative perception of tests as something that teachers oppose in general. The introduction of the new Common Core tests might have been welcomed by the teaching profession as a stronger alignment of the test with the widely shared belief about what is important for students to learn. But the change was opposed by the profession largely because it would be unfair to evaluate teachers on the basis of a test they had no experience preparing students for. Reducing the teaching profession’s opposition to testing may help reduce the clamor of the opt-out movement and keep the schools on the path of continuous improvement of student assessment.

We can return to recognizing that testing has value for teachers as formative assessment. And for the larger community it has value as assurance that schools and districts are maintaining standards, and most importantly, in considering the reauthorization of NCLB, not failing to educate subgroups of students who have the most need.

A final note. For purposes of program and policy evaluation, for understanding the elements of effective teaching, and for longitudinal tracking of the effect on students of school experiences, standardized testing is essential. Research on value-added modeling must continue and expand beyond tests to measure the effect of teachers on preparing students for “college and career”. Removing individual teacher evaluation from the equation will be a positive step toward having the data needed for evidence-based decisions.

An abbreviated version of this blog post can be found on Real Clear Education.

2015-09-10

Conference Season 2015

Empirical researchers are traveling all over the country this conference season. Come meet our researchers as we discuss our work at the following events. If you plan to attend any of these, please get in touch so we can schedule a time to speak with you, or come by to see us at our presentations.

AEFP

We are pleased to announce that we will have our fifth appearance at the 40th annual conference of the Association for Education Finance and Policy (AEFP). Join us in the afternoon on Friday, February 27th at the Marriott Wardman Park, Washington DC as Empirical’s Senior Research Scientist Valeriy Lazarev and CEO Denis Newman present on Methods of Teacher Evaluation in Concurrent Session 7. Denis will also be the acting discussant and chair on Friday morning at 8am in Session 4.07 titled Preparation/Certification and Evaluation of Leaders/Teachers.

SREE

Attendees of this spring’s Society for Research on Effectiveness (SREE) Conference, held in Washington, DC March 5-7, will have the opportunity to discuss instructional strategies and programs to improve mathematics with Empirical Education’s Chief Scientist Andrew P. Jaciw. The presentation, Assessing Impacts of Math in Focus, a ‘Singapore Math’ Program for American Schools, will take place on Friday, March 6 at 1pm in the Park Hyatt Hotel, Ballroom Level Gallery 3.

ASCD

This year’s 70th annual conference for ASCD will take place in Houston, TX on March 21-23. We invite you to schedule a meeting with CEO Denis Newman while he’s there.

AERA

We will again be presenting at the annual meeting of the American Educational Research Association (AERA). Join the Empirical Education team in Chicago, Illinois from April 16-20, 2015. Our presentations will cover research under the Division H (Research, Evaluation, and Assessment in Schools) Section 2 symposium: Program Evaluation in Schools.

  1. Formative Evaluation on the Process of Scaling Up Reading Apprenticeship Authors: Jenna Lynn Zacamy, Megan Toby, Andrew P. Jaciw, and Denis Newman
  2. The Evaluation of Internet-based Reading Apprenticeship Improving Science Education (iRAISE) Authors: Megan Toby, Jenna Lynn Zacamy, Andrew P. Jaciw, and Denis Newman

We look forward to seeing you at our sessions to discuss our research. As soon as we have the schedule for these presentations, we will post them here. As has become tradition, we plan to host yet another of our popular AERA receptions. Details about the reception will follow in the months to come.

2015-02-26

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.

2014-08-30

Empirical Education Helps North Carolina to Train and Calibrate School Leaders in the North Carolina Educator Effectiveness System

Empirical Education, working with its partner BloomBoard, is providing calibration and training services for school administrators across the state of North Carolina. The use of Observation Engine began in June with a pilot of the integrated solution, and once fully deployed, will be available to all 115 districts in the state, reaching more than 6,000 school leaders and potentially 120,000 teachers in the process.

The partnership with BloomBoard gives users an easy-to-use, integrated platform and gives North Carolina Department of Public Instruction (NCDPI) a comprehensive online training and calibration solution for school administrators who will be evaluating teachers as part of the North Carolina Educator Evaluation System (NCEES). The platform will combine Empirical’s state-of-the-art observer training and calibration tool, Observation Engine, with BloomBoard’s Professional Development Marketplace.

NCDPI Director of Education Effectiveness, Lynne Johnson, is excited about the potential for the initiative. “The BloomBoard-Empirical partnership is an innovative new approach that will help change the way our state personalizes the training, professional development, and calibration of our educators,” says Johnson. “We look forward to working with partners that continue to change the future of U.S. education.”

Read the press release.

2014-08-07

Factor Analysis Shows Facets of Teaching

The Empirical team has illustrated quantitatively what a lot of people have suspected. Basic classroom management, keeping things moving along, and the sense that the teacher is in control are most closely associated with achievement gains. We used teacher evaluation data collected by the Measures of Effective Teaching project to develop a three-factor model and found that only one factor was associated with VAM scores. Two other factors—one associated with constructivist pedagogy and the other with positive attitudes—were unrelated to short-term student outcomes. Val Lazarev and Denis Newman presented this work at the Association for Education Finance and Policy Annual Conference on March 13, 2014. And on May 7, Denis Newman and Kristen Koue conducted a workshop on the topic at the CCSSO’s SCEE Summit. The workshop emphasized the way that factors not directly associated with spring test scores can be very important in personnel decisions. The validation of these other factors may require connections to student achievements such as staying in school, getting into college, or pursuing a STEM career in years after the teacher’s direct contact.

2014-05-09

State Reports Show Almost All Teachers Are Effective or Highly So. Is This Good News?

The New York Times recently picked up a story, originally reported in Education Week two months ago, that school systems using formal methods for classroom observation as part of their educator evaluations are giving all but a very small percent of teachers high ratings—a phenomenon commonly known as the “widget effect.” The Times quotes Russ Whitehurst as suggesting that “It would be an unusual profession that at least 5 percent are not deemed ineffective.”

Responding to the story in her blog, Diane Ravitch calls it “unintentionally hilarious,” portraying the so-called reformers as upset that their own expensive evaluation methods are finding that most teachers are good at what they do. In closing, she asks, “Where did all those ineffective teachers go?”

We’re a research company working actively on teacher evaluation, so we’re interested in these kinds of questions. Should state-of-the-art observation protocols have found more teachers in the “needs improvement” category or at least 5% labeled “ineffective”? We present here an informal analysis meant to get an approximate answer, but based on data that was collected in a very rigorous manner. As one of the partners in the Gates Foundation’s Measures of Effective Teaching (MET) project, Empirical Education has access to a large dataset available for this examination, including videotaped lessons for almost 2,000 teachers coded according to a number of popular observational frameworks. Since the MET raters were trained intensively using methods approved by the protocol developers and had no acquaintance or supervisory relationship with the teachers in the videos, there is reason to think that the results show the kind of distribution intended by the developers of the observation methods. We can then compare the results in this controlled environment to the results referred to in the EdWeek and Times articles, which were based on reporting by state agencies. We used a simple (but reasonable) way of calculating the distribution of teachers in the MET data according to the categories in one popular protocol and compared it to the results reported by one of the states for a district known to have trained principals and other observers in the same protocol. We show the results here. The light bars show the distribution of the ratings in the MET data. We can see that a small percentage are rated “highly effective” and an equally small percentage “unsatisfactory.” So although the number doesn’t come up to the percent suggested by Russ Whitehurst, this well-developed method finds only 2% of a large sample of teachers to be in the bottom category. About 63% are considered “effective”, while a third are given a “needs improvement” rating. The dark bars are the ratings given by the school district using the same protocol. This shows a distribution typical of what EdWeek and the Times reported, where 97% are rated as “highly effective” or “effective.” It is interesting that the school district and MET research both found a very small percentage of unsatisfactory teachers.

Where we find a big difference is in the fact that the research program deemed only a small number of teachers to be exceptional while the school system used that category much more liberally. The other major difference is in the “needs improvement” category. When the observational protocol is used as designed, a solid number of teachers are viewed as doing OK but potentially doing much better. Both in research and in practice, the observational protocol divides most teachers between two categories. In the research setting, the distinction is between teachers who are effective and those who need improvement. In practice, users of the same protocol distinguish between effective and highly effective teachers. Both identify a small percent as unsatisfactory.

Our analysis suggests two problems with the use of the protocol in practice: first, the process does not provide feedback to teachers who are developing their skills, and, second, it does not distinguish between very good teachers and truly exceptional ones. We can imagine all sorts of practical pressures that, for the evaluators (principals, coaches and other administrators) decrease the value of identifying teachers who are less than fully effective and can benefit from developing specific skills. For example, unless all the evaluators in a district simultaneously agree to implement more stringent evaluations, then teachers in the schools where such evaluations are implemented will be disadvantaged. It will help to also have consistent training and calibration for the evaluators as well as accountability, which can be done with a fairly straightforward examination of the distribution of ratings.

Although this was a very informal analysis with a number of areas where we approximated results, we think we can conclude that Russ Whitehurst probably overstated the estimate of ineffective teachers but Diane Ravitch probably understated the estimate of teachers who could use some help and guidance in getting better at what they do.

Postscript. Because we are researchers and not committed to the validity of the observational methods, we need to state that we don’t know the extent to which the teachers labeled ineffective are generally less capable of raising student achievement. But researchers are notorious for ending all our reports with “more research is needed!”

2013-04-20

Can We Measure the Measures of Teaching Effectiveness?

Teacher evaluation has become the hot topic in education. State and local agencies are quickly implementing new programs spurred by federal initiatives and evidence that teacher effectiveness is a major contributor to student growth. The Chicago teachers’ strike brought out the deep divisions over the issue of evaluations. There, the focus was on the use of student achievement gains, or value-added. But the other side of evaluation—systematic classroom observations by administrators—is also raising interest. Teaching is a very complex skill, and the development of frameworks for describing and measuring its interlocking elements is an area of active and pressing research. The movement toward using observations as part of teacher evaluation is not without controversy. A recent OpEd in Education Week by Mike Schmoker criticizes the rapid implementation of what he considers overly complex evaluation templates “without any solid evidence that it promotes better teaching.”

There are researchers engaged in the careful study of evaluation systems, including the combination of value-added and observations. The Bill and Melinda Gates Foundation has funded a large team of researchers through its Measures of Effective Teaching (MET) project, which has already produced an array of reports for both academic and practitioner audiences (with more to come). But research can be ponderous, especially when the question is whether such systems can impact teacher effectiveness. A year ago, the Institute of Education Sciences (IES) awarded an $18 million contract to AIR to conduct a randomized experiment to measure the impact of a teacher and leader evaluation system on student achievement, classroom practices, and teacher and principal mobility. The experiment is scheduled to start this school year and results will likely start appearing by 2015. However, at the current rate of implementation by education agencies, most programs will be in full swing by then.

Empirical Education is currently involved in teacher evaluation through Observation Engine: our web-based tool that helps administrators make more reliable observations. See our story about our work with Tulsa Public Schools. This tool, along with our R&D on protocol validation, was initiated as part of the MET project. In our view, the complexity and time-consuming aspects of many of the observation systems that Schmoker criticizes arise from their intended use as supports for professional development. The initial motivation for developing observation frameworks was to provide better feedback and professional development for teachers. Their complexity is driven by the goal of providing detailed, specific feedback. Such systems can become cumbersome when applied to the goal of providing a single score for every teacher representing teaching quality that can be used administratively, for example, for personnel decisions. We suspect that a more streamlined and less labor-intensive evaluation approach could be used to identify the teachers in need of coaching and professional development. That subset of teachers would then receive the more resource-intensive evaluation and training services such as complex, detailed scales, interviews, and coaching sessions.

The other question Schmoker raises is: do these evaluation systems promote better teaching? While waiting for the IES study to be reported, some things can be done. First, look at correlations of the components of the observation rubrics with other measures of teaching such as value-added to student achievement (VAM) scores or student surveys. The idea is to see whether the behaviors valued and promoted by the rubrics are associated with improved achievement. The videos and data collected by the MET project are the basis for tools to do this (see earlier story on our Validation Engine.) But school systems can conduct the same analysis using their own student and teacher data. Second, use quasi-experimental methods to look at the changes in achievement related to the system’s local implementation of evaluation systems. In both cases, many school systems are already collecting very detailed data that can be used to test the validity and effectiveness of their locally adopted approaches.

2012-10-31

Need for Product Evaluations Continues to Grow

There is a growing need for evidence of the effectiveness of products and services being sold to schools. A new release of SIIA’s product evaluation guidelines is now available at the Selling to Schools website (with continued free access to SIIA members), to help guide publishers in measuring the effectiveness of the tools they are selling to schools.

It’s been almost a decade since NCLB made its call for “scientifically-based research,” but the calls for research haven’t faded away. This is because resources available to schools have diminished over that time, heightening the importance of cost benefit trade-offs in spending.

NCLB has focused attention on test score achievement, and this metric is becoming more pervasive; e.g., through a tie to teacher evaluation and through linkages to dropout risk. While NCLB fostered a compliance mentality—product specs had to have a check mark next to SBR—the need to assure that funds are not wasted is now leading to a greater interest in research results. Decision-makers are now very interested in whether specific products will be effective, or how well they have been working, in their districts.

Fortunately, the data available for evaluations of all kinds is getting better and easier to access. The U.S. Department of Education has poured hundreds of millions of dollars into state data systems. These investments make data available to states and drive the cleaning and standardizing of data from districts. At the same time, districts continue to invest in data systems and warehouses. While still not a trivial task, the ability of school district researchers to get the data needed to determine if an investment paid off—in terms of increased student achievement or attendance—has become much easier over the last decade.

The reauthorization of ESEA (i.e., NCLB) is maintaining the pressure to evaluate education products. We are still a long way from the draft reauthorization introduced in Congress becoming a law, but the initial indications are quite favorable to the continued production of product effectiveness evidence. The language has changed somewhat. Look for the phrase “evidence based”. Along with the term “scientifically-valid”, this new language is actually more sophisticated and potentially more effective than the old SBR neologism. Bob Slavin, one of the reviewers of the SIIA guidelines, says in his Ed Week blog that “This is not the squishy ‘based on scientifically-based evidence’ of NCLB. This is the real McCoy.” It is notable that the definition of “evidence-based” goes beyond just setting rules for the design of research, such as the SBR focus on the single dimension of “internal validity” for which randomization gets the top rating. It now asks how generalizable the research is or its “external validity”; i.e., does it have any relevance for decision-makers?

One of the important goals of the SIIA guidelines for product effectiveness research is to improve the credibility of publisher-sponsored research. It is important that educators see it as more than just “market research” producing biased results. In this era of reduced budgets, schools need to have tangible evidence of the value of products they buy. By following the SIIA’s guidelines, publishers will find it easier to achieve that credibility.

2011-11-12
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