IES Blog

Institute of Education Sciences

Putting Your Ideas into Action: Instructional Tips for Educators

By Christopher Weiss, Program Manager, What Works Clearinghouse

The What Works Clearinghouse (WWC) is always looking for ways to improve. We want it to be as easy as possible for our users to connect with the evidence they need, so they can make informed educational decisions.

Last year, we undertook a comprehensive, multi-faceted self-study. Through surveys, interviews, and focus groups, we asked a variety of different WWC users to tell us what we were doing well and, more importantly, what we could do better. (Click here if you’re interested in all the results.)

Some of the specific suggestions we received focused on the WWC Educator’s Practice Guides, which combine the best available research evidence and practitioner expertise on a topic to provide educators with strategies to use in their school or classroom. Based upon a review of the research literature and the guidance of a panel of nationally recognized experts, practice guides synthesize evidence and the wisdom of practitioners.

One particular suggestion that came from the self-study was to create a separate, stand-alone document with concise and specific information that a teacher or school would need to carry out some of a practice guide’s recommendations. It was a great suggestion – and we put it into action.

On July 25, we released our first Instructional Tips publication (PDF), which was created to help educators carry out the recommendations in the Improving Mathematical Problem Solving in Grades 4 through 8 practice guide. We provide tips for three of the Practice Guide’s five recommendations:

  • Assisting Students in Monitoring and Reflecting on the Problem-Solving Process;
  • Teaching Students to Use Visual Representations to Solve Problems; and
  • Helping Students Make Sense of Algebraic Notation.

As an example, for the recommendation on visual representations, we offer two instructional tips. First, we suggest that teachers demonstrate how to select the appropriate visual representation for the problem they are solving and we provide specific steps and examples for implementing this tip. Second, we suggest teachers use think-alouds and discussions to teach students how to represent problems visually and, again, provide specific steps and work examples. Here's one of the examples from the publication:

An accompanying document (PDF) to the Instructional Tips describes the evidence base that supports these recommended practices.

We are planning additional Instructional Tips publications down the road, but we want to hear from you first. If you have questions or ideas for how we can improve this resource, we’d love to hear them. Please send them through an email to the WWC Help Desk.

The Instructional Tips are just one of several ways we are working to improve the WWC. Over the past two years, we have redesigned our website and created a new Find What Works tool to make it easier for users to find the evidence they need. We have also increased our use of Facebook and Twitter to help us better connect with new audiences; published new briefs and held several webinars to explain WWC processes and resources; and have launched a new Reviews of Individual Studies database to give the field quicker access to the research we have reviewed. And all of this has been done while we continue to identify interventions, practices and programs that show evidence of improving student outcomes across a wide array of educational topics.

Stay up to date on new WWC products, events, and resources by signing up for the IES News Flash (under NCEE) and following us on Facebook and Twitter

IES Funds New Research in Career and Technical Education

The Institute of Education Sciences (IES) funds research in a broad array of education topics. In fact, the Education Research Grants Program alone funds research in 11 specific topics, such as early learning, reading and writing, STEM, postsecondary and adult education, English learners, social behavioral contexts for learning and others.

In 2017, the National Center for Education Research (NCER) introduced a twelfth area, Special Topics, to address important areas in education that are of high interest to policy makers and practitioners where there is a research gap.

As we noted in a previous blog, Career and Technical Education (CTE) is one such area. Across the country, CTE programs and policies are growing, creating a greater need for high-quality, independent research in this area. The Career and Technical Education (CTE) special topic seeks to fill this research gap by funding projects that study the implementation of CTE programs and policies and how they impact student outcomes in K-12 education. In 2017, IES has funded its first three special topic research grants on CTE:

  • New York University will study the impact of New York City's Career Technical Education programs on students' career and work-related learning experiences, social and behavioral competencies, high school completion, and transitions to college and the work place;
  • The Education Development Center will lead a study that compares three different ways that CTE is delivered in California—career academies, career pathways, and elective CTE courses. The researchers will examine relationships between CTE delivery mode and student outcomes; and
  • A study of Florida’s CTE certification program will be conducted by Research Triangle Institute (RTI). The study will identify which high school certifications are associated with a higher likelihood of passing certification exams and whether obtaining a certification leads to better attendance, graduation rates, and postsecondary enrollment and persistence.

For its 2018 grant competition, IES is again accepting applications for CTE research grants, as well as two other special topics.

The Arts in Education special topic funds research to better understand how arts programs and policies are implemented and the impact they have on student outcomes. The research coming out of this program can help inform policy debates regarding the benefits of arts programming in schools. (Read a recent blog post on this topic.)

The Systemic Approaches to Educating Highly Mobile Students special topic seeks to fund research aimed at improving the education and outcomes for students who frequently move schools because of changes in residence and/or unstable living arrangements. This includes students who are homeless, in foster care, from migrant backgrounds or are a part of military families. (Read a recent blog post on this topic.)

You can learn more about these and other funding opportunities on the IES website, and on Facebook and Twitter

Written by Dana Tofig, Communications Director, IES

Student homelessness in urban, suburban, town, and rural districts

Data from two recent NCES reports—the Condition of Education and the Digest of Education Statistics—show that student homelessness is a challenge in many different types of communities.

In 2014-15, the rate of homelessness among U.S. public school students was highest in city school districts at 3.7 percent, but was also 2.0 percent or higher in suburban, town, and rural districts. While suburban districts had the lowest rate of student homelessness, they still enrolled 422,000 homeless students, second only to the 578,000 homeless students enrolled in city districts. Smaller numbers of homeless students were enrolled in rural (149,000) and town (139,000) districts.


Figure 1. Percentage of public school students who were identified as homeless, by school district locale: School year 2014–15

NOTE: Homeless students are defined as children/youth who lack a fixed, regular, and adequate nighttime residence. For more information, see "C118 - Homeless Students Enrolled" at https://www2.ed.gov/about/inits/ed/edfacts/sy-14-15-nonxml.html. Data include all homeless students enrolled at any time during the school year. Data exclude Puerto Rico and the Bureau of Indian Education.
SOURCE: U.S. Department of Education, National Center for Education Statistics, EDFacts file 118, Data Group 655, extracted January 23, 2017, from the EDFacts Data Warehouse (internal U.S. Department of Education source). Common Core of Data (CCD), "Local Education Agency Universe Survey," 2014–15. See Digest of Education Statistics 2016, table 204.75b.


The majority of students experiencing homelessness (76 percent) were doubled up or sharing housing with other families due to loss of their own housing, economic hardship, or other reasons such as domestic violence. Seven percent were in hotels or motels; 14 percent were in shelters, transitional housing or awaiting foster care placement; and 3 percent were unsheltered.

The percentage of homeless students who were doubled up with other families ranged from 70 percent in city districts to 81 percent in rural districts. The percentage of homeless students who were housed in shelters was higher in city districts than in suburban, town, and rural districts. The percentages of homeless students who were unsheltered or living in hotels and motels varied less widely across district locale categories.


Figure 2. Percentage distribution of public school students who were identified as homeless, by primary nighttime residence and school district locale: School year 2014–15

1Refers to temporarily sharing the housing of other persons due to loss of housing, economic hardship, or other reasons (such as domestic violence).
2Includes living in cars, parks, campgrounds, temporary trailers—including Federal Emergency Management Agency (FEMA) trailers—or abandoned buildings.
NOTE: Homeless students are defined as children/youth who lack a fixed, regular, and adequate nighttime residence. For more information, see "C118 - Homeless Students Enrolled" at https://www2.ed.gov/about/inits/ed/edfacts/sy-14-15-nonxml.html. Data include all homeless students enrolled at any time during the school year. Data exclude Puerto Rico and the Bureau of Indian Education. This figure is based on state-level data.
SOURCE: U.S. Department of Education, National Center for Education Statistics, EDFacts file 118, Data Group 655, extracted January 23, 2017, from the EDFacts Data Warehouse (internal U.S. Department of Education source). Common Core of Data (CCD), "Local Education Agency Universe Survey," 2014–15. See Digest of Education Statistics 2016, table 204.75b.


The percentage of homeless students who were unaccompanied youth–meaning that they were not in the physical custody of a parent or guardian—was   highest in rural districts (9.3 percent) and lowest in suburban districts (6.9 percent). The percentage of homeless students who were English language learners was highest in urban districts (16.8 percent) and lowest in rural districts (5.9 percent), and the percentage who were migrant students was highest in town districts (3.4 percent) and lowest in urban districts (1.0 percent).

Data used in this analysis were collected under the McKinney-Vento Homeless Assistance Act of 1987. This legislation requires that school districts identify students experiencing homelessness and guarantees students’ right to enroll in public schools and access educational and transportation services. More information on this legislation and the U.S. Department of Education’s programs and resources focused on student homelessness can be found on the National Center for Homeless Education’s website.

States report aggregated data on homeless students to the U.S. Department of Education through the EDFacts collection. EDFacts covers all public school districts and provides a uniquely detailed view of student homelessness. The full data on student homelessness by school district locale is available in the Digest of Education Statistics. A broader analysis in the Condition of Education describes how student homelessness has changed over time and how it varies among states. You can view homeless student data for the 120 largest school districts here and download a dataset with information on all public school districts here.

 

 

Risk Factors and Academic Outcomes in Kindergarten through Third Grade

By Amy Rathbun, AIR and Joel McFarland

Previous NCES research has shown that students with family risk factors tend to have lower average scores than their peers on academic assessments.[1] Risk factors can include coming from a low-income family or single-parent household, not having a parent who completed high school, and living in a household where the primary language is not English. How common is it for children entering U.S. kindergartens to have certain types of family risk factors? And, how do children with risk factors at kindergarten entry perform on academic assessments compared to their peers?  A new spotlight from The Condition of Education 2017 helps to answer these questions.

The spotlight focuses on children experiencing two types of risk factors - living in poverty (i.e., in households with income below the federal poverty threshold) and not having a parent who completed high school, as well as the combination and lack of the two risk factors. Data come from the Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011). During the 2010–11 school year, 6 percent of first-time kindergartners had both risk factors , 18 percent had the single risk factor of living in poverty, and 2 percent had the single risk factor of not having a parent who completed high school. About 75 percent had neither of these two risk factors present during their kindergarten year.


Percentage distribution of fall 2010 first-time kindergartners, by risk factors related to parent education and poverty: School year 2010–11

NOTE: Detail may not sum to totals because of rounding.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), Kindergarten–Third Grade Restricted-Use Data File. See Digest of Education Statistics 2016, table 220.39.


Are there differences in the prevalence of risk factors by student and family characteristics?

There were differences in the prevalence of family risk factors in relation to children’s race/ethnicity, primary home language, and family composition. For instance, it was more common for Hispanic students (15 percent) than for Black and Asian students (8 percent each) to have both risk factors, and these percentages were all higher than the percentage for White students (1 percent). Also, 23 percent of first-time kindergartners whose primary home language was not English had both the risk factor of living in poverty and the risk factor of not having a parent who completed high school, compared with 2 percent of kindergartners whose primary home language was English.

Does children’s performance in reading, math, and science in kindergarten through third grade differ based on risk factors?

Kindergarten students living in poverty and those with no parent that completed high school tended to score lower in reading, mathematics, and science over each of their first four years of school compared to their peers who had neither risk factor at kindergarten entry. For example, in the spring of third-grade, reading scores (on a scale of 0 to 141) were higher, on average, for students who had neither risk factor (114 points) than for those with the single risk factor of living in poverty (106 points), those with the single risk factor of not having a parent who completed high school (105 points), and those with both risk factors (102 points).[2]


Average reading scale scores of fall 2010 first-time kindergartners, by time of assessment and risk factors related to parent education and poverty: Fall 2010 through spring 2014

NOTE: Estimates weighted by W7C17P_7T170. Scores on the reading assessments reflect performance on questions measuring basic skills (print familiarity, letter recognition, beginning and ending sounds, rhyming words, and word recognition); vocabulary knowledge; and reading comprehension, including identifying information specifically stated in text (e.g., definitions, facts, and supporting details), making complex inferences from texts, and considering the text objectively and judging its appropriateness and quality. Possible scores for the reading assessment range from 0 to 141.
SOURCE: U.S. Department of Education, National Center for Education Statistics, Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), Kindergarten–Third Grade Restricted-Use Data File. See Digest of Education Statistics 2016, table 220.40.


For more information on family risk factors and children’s achievement in reading, mathematics, and science from the fall of kindergarten through the spring of third grade, see the spotlight on this topic in The Condition of Education 2017.

[1] Given that the spring third-grade reading scores have a mean of 110.2 points and a standard deviation (SD) of 12.3 points, this would mean the average score for children who had no risk factors was about 1.0 SD higher than the score for children with no risk factors.

[2] Rathbun, A., and West, J. (2004). From Kindergarten Through Third Grade: Children's Beginning School Experiences (NCES 2004–007). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved March 2, 2017, from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2004007.

 

 

Understanding Outcomes for English Learners: The Importance of the ‘Ever EL' Category

The Institute of Education Science funds and supports Researcher-Practitioner Partnerships (RPP) that address significant challenges in education. In this guest blog post, Karen D. Thompson, of Oregon State University and Josh Rew, Martha Martinez, and Chelsea Clinton, of the Oregon Department of Education, describe the work their RPP is doing to better understand and improve the performance English learners in Oregon. Click here to learn more about RPP grants. This research will be part of a Regional Educational Laboratory webinar on June 21.


According to the most recent data, about 10 percent of K-12 students in U.S. public schools were classified as English learners (EL). But that only tells part of the story: a large proportion of students in U.S. schools are former ELs, who have attained proficiency in English and “exited” EL services. Currently, in most states and the nation, we do not know the size of the former EL group because states have only been required to monitor this group of students for a limited amount of time.

Education agencies and the media routinely report the achievement gap between current EL students and their non-EL peers. However, analyzing outcomes only for current EL students does not provide a complete picture of how well schools are serving the full group of students who entered school not yet proficient in English. We refer to this full group, which includes both current and former ELs, as Ever English Learners (Ever ELs).

Through our IES-funded partnership, the Oregon Department of Education (ODE) and Oregon State University (OSU) has identified the full group of both current and former ELs in Oregon public K-12 schools. Using 2015-16 Oregon data, we looked at the proportion of Ever ELs who are current and former ELs at each grade level. As seen in Figure 1 below, former ELs outnumber current ELs in grades 6 and above, with the relative size of the former EL population increasing at each grade level.


Figure 1


Starting with the 2012-13 school year, ODE began annually reporting to the public the outcomes of Ever ELs (e.g., achievement and growth, chronic absenteeism, rates of freshmen on-track, and graduation rates). These annual reports include school and district report cards, the statewide report card, and technical reports corresponding to specific state initiatives, such as graduation rates, chronic absenteeism, assessment participation, and district EL accountability. 

In the past, states have typically reported achievement outcomes for students currently classified as ELs and compared these to outcomes for all students not currently classified as ELs. Under this reporting scheme, the non-EL subgroup consists of students never classified as ELs and former ELs.  With this grouping (Figure 2), graduation rates for ELs appear much lower than graduation rates for non-ELs (52.9 percent for ELs compared to 75.8 percent for non-ELs).           

  

However, it may be more appropriate in some situations to instead analyze outcomes for the full group of students who entered school as ELs (Figure 3). Under this alternative reporting scheme, if we combine outcomes for both current and former ELs to create the Ever EL group, we see that graduation rates for Ever ELs are much closer to graduation rates for students never classified as ELs (71.1 percent for Ever ELs compared to 75.6 percent for Never ELs).

While the low graduation rates for current ELs are certainly concerning, it is also important to know that former ELs are graduating at rates slightly higher than students never classified as ELs (77.9 percent vs. 75.6 percent, respectively), as shown in Figure 4.

This is particularly noteworthy since former ELs represent a larger proportion of the student population than current ELs at the secondary level.

In addition to annual reporting, the ODE began using data for Ever ELs in 2015-16 to identify districts in need of support, assistance, and improvement, as required by state law.  The state’s accountability system identifies the districts with the highest needs and lowest outcomes as measured by demographic indicators (such as economically disadvantage, migrant or homeless status) and outcome data (e.g., growth, graduation, and post-secondary enrollment) for Ever ELs. Identified districts conduct a needs assessment, identify evidence-based and culturally responsive technical assistance, develop a technical assistance implementation plan, monitor progress, and review outcomes and make necessary adjustments. Along with its applications for reporting and accountability, we have used the Ever EL framework to analyze special education disproportionality, documenting implications for research, policy, and practice.

To learn more about how education agencies are using the Ever EL category, join us and colleagues from New York City for a June 21 webinar, sponsored by Regional Educational Laboratory Northeast and Islands.