How can we use a systematic approach to select students for intervention?  How do we go about matching students with an intervention?  Using data to make decisions about a multi-tiered system of supports can be complicated, especially within a biliteracy framework.  Too often, we interpret data to mean that many of our language learners need to receive a literacy intervention in order to progress.  Or, we rely on data from just one language to guide us.   In this blog post, I will share a process for using data in both languages to inform decisions about supports and intervention.

Step 1: Prepare cards or post-its that represent each student’s key information

At our school, we carry out this process with one grade level of students (about 54 students).  We begin by making a small information card of each student.  One way we’ve found to humanize this work is to include an actual photograph of the child.  We color code the card according to the child’s linguistic profile .  (You might remember Cheryl and Karen’s blue and green face presentations – see Chapter 2 in Teaching for Biliteracy for more information)

Step 2:  Begin with universal screening data in one language and do an initial sort.

Most schools have a reading assessment that is administered to all students and that gives a quantitative score. In our school district, this could be PALS, AIMS, MAP, or text reading level data.  Often times, this data is more readily available in English, so we start there. We indicate the benchmark score for that grade level at that time of year on the vertical line.  We display students who have met the benchmark on the left-hand side, and those who have not on the right-hand side.   For lack of better terms, we label the columns “high” and “low”.

Step 3: Now look at the other language and do a secondary sort.

If possible, we choose data from the same assessment as above, but in the other language. For example, if we sorted text reading level data in English, it would make sense to now sort text reading level data in Spanish.  This activity can still be used using two different forms of assessments.  In our third, fourth, and fifth grades we do our initial sort using MAP English data and our second sort using text reading level data in Spanish.  The important thing is to indicate the proficiency benchmark for that assessment on the horizontal line, and to move students accordingly.

Step 4:  Analyze the data, one quadrant at a time.

High/High quadrant

It’s usually best to start with a positive root cause analysis of what we are doing that is working well for many students.  We address the students in the “high” “high” quadrant. This is an opportunity to consider both our teaching practices and larger systems that impact student achievement. For example, we notice across our grades that our ELL students are often underrepresented in this quadrant.  This can lead to a conversation about our school improvement plan, allocation of resources, etc.

High/low and Low/high quadrants:

Students that fall in the high/low or low/high quadrants call for us to consider two important factors. First,  is the student’s reading performance due in large part to his/her oral language development? If so, this child best support for the child might be in oracy. (See blog posts  RtI and Biliteracy: how can we gather language on oral language development? and Language Development and Literacy Processing.)  Second, let’s embrace a multi-lingual perspective and consider strengths from each language on which to build.  Often, students in these quadrants can benefit from explicit teaching for transfer of a reading behavior they are exhibiting in one language but not the other.  (See blog post Below the Tip of the Iceberg.)

Low/low quadrant

Most would agree that our students of highest concern are students in the “low” “low” quadrant.  These students are those we prioritize as candidates for literacy intervention.  Often, the challenges of these students are due to literacy processing and not solely language development. See blog post from Spring, 2016.   It is important to note that the challenges might reflect simply not having access to learning to read in that language, depending on the bilingual model and the language of literacy instruction at your school.

Using a process like the above for selecting students for intervention can be tedious work, but it’s important. This  approach can prevent bilingual learners from being hastily tagged for intervention.  When we analyze data across languages, we position ourselves to capitalize on oral language resources and to teach for transfer of skills across languages. These strategies are perhaps a more efficient (and strength-based) approach than targeted reading intervention! Perhaps most importantly, this process can point out those few students who are most in need of the most support.

No tears about tiers – there are more important issues we can be crying about!