Shadowing Multilingual Learners
My version of shadowing multilingual learners is carefully coding students’ listening, speaking, reading and writing during the course of one or more classes. When schools want to better understand the impact of instructional moves on multilingual learners’ language growth, I find that shadowing students can reveal some of the most insightful data and lead to significant instructional shifts. After nearly two years of disrupted education due to a global pandemic, student shadowing can provide valuable insights into how multilingual learners are engaging in classroom content and developing their language.
The shadowing tool I modified is based on the one first developed by Ivannia Soto for the Los Angeles Unified School District in 2003 for the ELL shadowing project (p. 11). Dr. Soto describes how to use that tool to shadow students in her 2012 book ELL Shadowing as a Catalyst for Change. The shadowing tool I modified from this work codes multilingual learners’ speaking, listening, reading, and writing in academic and social contexts. You can also watch my 30-minute webinar called Using ELL Shadowing as a Catalyst for Change with more details about using the shadowing tool. The webinar title is also borrowed from the book that inspired this work.
Educators can shadow one student throughout a school day or multiple students in several different classes, depending on the purpose for gathering the data. In order to shadow a student, teachers will have to be observers in the class. They cannot teach the class and shadow a student at the same time.
Why Shadow Multilingual Learners?
Shadowing a student during a class period provides a student-centered lens on teaching and learning. As Shane Safir and Jamila Dugan write in their book Street Data: A Next-Generation Model for Equity, Pedagogy, and School Transformation, “There is perhaps no better way to empathically understand a student’s experience than to put on your tennis shoes and shadow him or her” (p. 63). Observing how multilingual learners are using language to speak, read, or write during class can reveal their instructional needs or indicate how they are included or excluded throughout their school day. For example, shadowing a group of multilingual learners at different grade levels or shadowing one specific subgroup of multilingual learners such as Newcomers, Long-Term English Learners, or Students with Interrupted Formal Education can provide a snapshot of engagement. This shadowing data gathered from multiple students across many classes can also indicate next steps for instruction or professional learning. It can also provide educators valuable insights into students’ performance on standardized achievement and language proficiency assessments.
How to Shadow Multilingual Learners
There are many methods for gathering shadowing data on multilingual learners. If you choose to use the tool I’ve developed or modify it for your purposes, I recommend that teams of educators gather shadowing data on multiple multilingual learners for at least two to three hours during one day.
Before a team of educators begins to shadow students, everyone needs to agree on how to use the shadowing tool to code during the observation. I’ll walk you through how to use the tool, and I encourage you to make changes to this form to better meet your needs. All codes described below are also written at the bottom of the shadowing tool and explained on page two of this form as well.
At the top of each minute, take a mental snapshot of what the student is doing in that moment. If the student is speaking, decide if they are using social or academic language in English or the home language. If you are in the WIDA Consortium, any use of language that would be considered part of Standard 1 “Language for Social and Instructional Purposes” would be coded as social language. For example, if students are talking about classroom instructions such as, “What page are we on?”, I code that utterance as social language. However, if the student is talking about the content or text, I code that speaking as academic, even if the words they are using sound like social language. When students use their entire linguistic repertoire to talk about academic content, I believe this is indeed academic language use. As a team, you can decide what counts as social and academic language for your purposes.
Next, determine if the student is speaking in words or phrases (WP), a complete sentence (CS), or extended discourse (ED) or multiple linked sentences. Also note if the student is speaking to the teacher (T) or another student (S). Now enter your code(s) in the appropriate triangle of the speaking column. For example, if the student was speaking in English to a partner using a complete sentence about the content of the math lesson, I would code S (student) CS (complete sentence) in the academic triangle under speaking in the first row of the form. However, if the student was speaking in many sentences in their home language to the teacher about the weekend, I would code T (teacher) ED (extended discourse) HL (home language) in the social triangle of the speaking column on row one. After I have coded that first snapshot at the top of the minute, I wait until the top of the next minute to code again. Only code in one column for each minute (see below).
Let’s assume that in the snapshot at the top of the first minute of observation, the student was speaking. Then the teacher says, “Read the next paragraph in your book.” At the top of the next minute, I note what the student is doing. If they are independently looking at the next paragraph in the book, I make a check mark in the “independent” triangle in the reading column in row two on the form. If they are looking around or clearly not reading, I mark MO for “missed opportunity” in the independent reading space. The “missed opportunity” code shows that the teacher gave students the opportunity to read independently, but the student I am shadowing missed that opportunity for whatever reason. Some teachers might consider this off task, but it’s hard to make that judgement call in such a short observation. Sometimes teachers will ask students to read chorally or provide a guided reading experience. These can be coded in the top triangle in the reading column (see below).
When I code students during writing, I determine if they are writing independently or if they are copying from a source or using sentence frames. So, if the teacher has asked students to work on an essay, I might notice the student with a pencil in hand looking around or simply staring at the computer screen, but not actively writing or typing. The student may be thinking of what to say next or searching for a word, but they aren’t actively writing at the top of the minute when I am coding. If I notice this behavior, I generally wait a few seconds before coding to see if the student will actually start writing. If not, I code this as a missed opportunity (MO) under independent writing. If the teacher has provided sentence frames, a word bank, or other writing supports that the student can copy from, I code that in the top triangle (copy/frames) on the writing column for that minute (see below).
Just like I code speaking, I code listening as social or academic using the same criteria. If the teacher is describing how to complete an activity or giving classroom instructions, I code that as social language according to WIDA’s Standard 1 “Language for Social and Instructional Purposes.” If the teacher or students are talking about the content, I code the listening incident as academic. I also code whether the student is listening to the teacher (T) or to another student (S) and if the student is listening to someone speaking in the home language (HL).
When coding listening, I do have to make a judgement about whether the student is actually listening or whether in the moment I observed the student was missing an opportunity to listen. I suggest you and your team decide what will constitute a missed opportunity and what will count as listening. For example, a Kindergarten student who is picking at the carpet or fidgeting with a shoelace may well be listening and following along, but a high school student who is looking at their cell phone may not be listening.
Now you are ready to shadow your multilingual learners and gather student-centered data on their language usage! Grab a timer that shows seconds, a copy of the shadowing form, and a clipboard. Determine which student you will shadow in the classroom and position yourself close enough to hear them speaking, but try not to give away which student you are watching; observe other students in between each coded interval. After coding your student for about 10 minutes (10 lines on the shadowing form), leave the classroom and check in with your colleagues about your coding questions before going in to the next classroom.
Use the column for Student and Teacher Activity Notes for any additional helpful information. I generally don’t use this information in the final data collection, but it can help provide context for the data collected. Everyone may have coded differently since everyone was observing a different student, but your coding forms should all show one code per line.
Organizing the Data
After you have observed several students in different classes over the course of at least a couple of hours, you will have many shadowing forms. At this point, I create a spreadsheet to tally the totals for each category. Here’s an example of those categories and the totals.
One school I worked with created a google form for each language domain (speaking, reading, writing, listening) which they used to collect the data. This made organizing the data points much faster after the observations.
Analyzing the Data
Creating pie charts from the spreadsheet or google forms that compare different categories gives educators an easy way to analyze the data. One elementary school I worked with was focused on developing the oral language skills of their multilingual learners. We shadowed students in the fall to gather baseline data of oral language use as shown below. When teachers reviewed this pie chart, they made a commitment to expanding students’ speaking beyond the word/ phrase level. They decided to ask more open-ended questions, challenge students to explain their thinking, and teach students how to have academic discussions in small groups.
After 3 months of focusing on these goals, we shadowed students again and found the following results. Changes like the ones shown below came about because teachers focused on what students were doing during class. Teachers created their own action plan based on the needs of the students and strategies they knew could work in their school context.
Another school I worked with was particularly focused on developing reading skills of their students classified as Long-Term English Learners (LTELs). They had implemented several different programs to encourage independent reading including a Drop Everything and Read (DEAR) time, but they still hadn’t seen any differences in student reading achievement. We decided to gather shadowing data focused on those students classified as LTELs to see if we could figure out why the programs weren’t making a difference. When the leadership team reviewed the following pie charts, they noticed that students spent about 20% of their class time reading. However, of this time that they were given to read in class, students were missing the opportunity to read over half the time. When teachers saw the data from the second pie chart below, they began to brainstorm root causes, implications, and next steps for their instruction. They suggested that students may need more scaffolds, supports, or guided instruction instead of just more time for independent reading. When they implemented those changes during their independent reading time in the following weeks, they noticed significantly more engagement in reading and an increase in reading comprehension.
These are just two examples of the insights teachers can gain and impact student shadowing can have on instruction, engagement, and programming for multilingual learners. If schools are looking for student-centered data on how students are engaging in learning, I recommend that leadership teams invest in the time to shadow several students and share the resulting data with the staff. As Principal Nathaniel Provencio states in this short video on the ColorinColorado website, shadowing a student was the “best professional development I ever had.” When teachers have the opportunity to determine implications of the data and next steps for their own instruction, they can make positive changes in students’ learning experiences.
Safir, S. and Dugan, J. (2021). STREET DATA: A next-generation model for equity, pedagogy, and school transformation. S.l.: SAGE Publications.
Soto, I. M. (2012). ELL Shadowing as a Catalyst for Change. Thousand Oaks: SAGE Publications.