Using Data-driven Decision Making to Support English Learner Students

REL Pacific
August 2, 2021

This blog is based on Part 1 of our recent two-part webinar series on Effective Data Use to Support English Learner Students, presented by our colleagues Eric Crane and Elizabeth Burr from WestEd.

Data-driven decision making, a systematic process for collecting and analyzing data regularly as part of the education decision-making process, is a critical strategy for addressing the varying needs of different groups of students, including English learners. Put simply, data-driven decision making re-quires two essential components, each of which can be complex and multifaceted: technology tools and human capacity.¹ Technology tools, such as data warehouses, student information sys-tems, instructional management systems, and assessment systems, provide for the collection, stor-age, analysis, and reporting of data. Human capacity, or data literacy, allows individuals and systems to inform and change practice or redistribute resources based on data. In other words, if technology is the “what?,” of data-driven decision making, human capacity is the “what's next?”

Creating a Culture of Data Use

Creating a culture of data use can help facilitate consistent data-driven decision making by setting a shared expectation for attitudes, values, goals, norms of behavior, and practices, accompanied by an explicit vision for data use by leadership for the importance and power that data can bring to the decision-making process.² Mandinach (2012) identified five essential characteristics of a culture of data use in education settings:

• Data are part of an ongoing cycle of instructional improvement.
• Students learn to examine their own data and to set their own learning goals.
• District or school leaders and staff, along with the community, set an explicit vision for how data should be used in the district or school.
• Supports and resources are provided to establish and sustain a data culture within schools, including setting aside time for teachers to review and have discussions based on data.
• The state or jurisdiction is responsible for developing and implementing a systemwide data system.³

Embedding expectations for data use within the system can have multiple benefits, fostering collaboration within a school community, enabling educators to more easily spot data patterns (or shifts in patterns) and adjust their instruction accordingly, and flagging potential areas for professional development.⁴

Not All English Learners are Alike

When we're considering how to use data-driven decision making to inform practice for English learner students, it's important to keep in mind that there are different kinds of English learner students. When comparing students' performance and progress to those of other students, it helps to have the closest comparison that the data allow, which requires comparing within type.⁵ For example, English learner students may include newcomers, or students with limited or interrupted formal education, long-term English learners, or English learner students with disabilities.⁶ Although some datasets may not allow for these within-type comparisons, being able to make precise com-parisons can make it easier to make inferences about an individual student's performance or progress and from there, to best decide what supports that student may need.

Key Data for Monitoring the Progress of English Learner Students

For data to be effective and useful, they need to be clear and unambiguous, consistent (the same data are collected over time so change can be measured), feasible (connected to potential actions), and meaningful.⁷

So what are some key data for monitoring the progress of English learner students?

• Content assessment results: English language arts, mathematics, other content areas.
• English language proficiency assessment results.
• Teacher reports of student's language learning progress: Teachers are often the best source of data for how students' English language skills are evolving. And in fact, if we can get observations in more than one setting, then we can have a more complete picture of the student's level of language mastery. And again, teachers can often identify the difference between a student's conversational English and academic English, so how a student is talking on the playground, for example, versus with the more academic terms in the classroom.
• Family input: Surveys, interviews, informal conversations with parents or other caregivers.
• Students' cumulative files: Report cards, attendance, behavior history, participation in programs or academic interventions, primary language, English language proficiency.
• Data on other factors: health, environment, past education, trauma.

Questions to Consider

The following questions can help guide your conversations as you think about how to best use data to support English learner students within your own context:

• In your state or jurisdiction, are data available that allow you to compare the performance of English learner students to other student groups?
• Do the data allow for finer comparisons to true peers?
• What do the data say about the time it takes English learner students in your context to become proficient in English?
• For students who continue to receive English learner services between five and seven years, what did the data say about their performance?
• How large a group within the English learner student group is that group of long-term English learners, and how do they perform on other measures?
• If the data don't allow some of these questions to be answered, what would it take to get there?

For more on this topic, view the full webinar, and stay tuned for our next blog on considerations for English language learners within an assessment system!

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Footnotes:

¹ Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice, Educational Psychologist, 47(2), 71–85.

² Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009–4067). National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/PracticeGuide/dddm_pg_092909.pdf

³ Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision making to inform practice, Educational Psychologist, 47(2), 71–85.

⁴ Gerzon, N. (2015). Structuring professional learning to develop a culture of data use: Aligning knowledge from the field and research findings. Teachers College Record, 117(4).

⁵ Brown, J., & Doolittle, J. (2008). A cultural, linguistic, and ecological framework for Response to Intervention with English language learners. Teaching Exceptional Children, 40(5), 66–72.

⁶ Ibid.

⁷ Crane, E. & Sigman, D. (2018). Using accountability data to guide school improvement. Presentation to the Insular Areas annual technical assistance meeting, 16 April 2018. Washington, DC: Author.