Going around the roadblocks

Last week when I was co-facilitating a professional development (“Using Data to Target Instruction and Intervention for English Language Arts”), we asked teachers to brainstorm a list of “roadblocks” to using  data to make instructional decisions. Time was definitely a roadblock that every one agreed is really a problem; however, we later created a “Like Me” graph, using the top seven choices, “Time” being one of those choices, and it turned out when faced with picking the top four roadblocks to data use, time was not one of the major choices (See photo. Time is the second row from the left).

When I asked teachers why time they did not see time as a major roadblock in this list, they stated because there wasn’t anything you could do about time. They never had enough time, so what else could you do about it. This thinking made me stop. I had been thinking that people have to be shown how to best maximize their time with the limited amount they have. I believe that these teachers were thinking that out of the choices they could make, how to resolve the time issue, was not a choice they would waste their energy on because there was really nothing they could do about it.

It’s true. Time can be our Waterloo in education. We never seem to have enough of it; we don’t have the money to pay teachers the amount of time we need them; and, when working with data, especially when you are starting out, it TAKES TIME! So with the lack of time, comes the lack of patience, which I see as definitely as  obtrusive as time: they are inextricably linked. When teachers don’t have enough time to complete the assigned task, they lose patience, becoming more and more frustrated, less and less patient with the data analysis process.

Data analysis can become a very time consuming task, especially if you do not have specific goals or structures in place. With very targeted goals, realistic expectations, an understanding of the time limitations, and strong organization and preparation, the issue of time can be addressed much more effectively.  We have to learn to work better with the limited time and resources that we have. This preparation should start taking place before the end of the school year, so in the summer, professional development of data analysis can be developed.  Examining data without specific goals or questions will only be an exercise in continual “data admiration,” which becomes a waste of time,  leading to frustration. Specific data questions and problem solving goals assist in designing the training, meetings, and documentation needed to analyze and act upon the data.

I really don’t believe that I can “blow up” the time roadblock, but one of my goals is to help schools construct a path around this roadblock so that it doesn’t stop the flow of information, knowledge, ideas, and actions.

The problem is occurring because…

Group members first decided which "reasons" they have no control over (can't change). Then they organized their hypotheses into categories: Instruction/Curriculum/Environment/Learner. After doing this step, they chose their top three (stars).

This group took their cue from another teacher in the class. Yesterday John stated that if you first look at your problem residing in the Learner, then you will get "lice."

This group chose their top three hypotheses, rewrote them and then also attached their prediction statements.

I and another facilitator have been working with twelve amazing educators in a Professional Development titled, “Using Data to Target Instruction and Intervention in English Language Arts.” These posters reflect the work we have been doing when working in step two of the Problem Solving Process: Problem Analysis.  The teachers were working with the Affinity Protocol as described in the book Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning.

Protocols provide a structure in which a group can have a constructive conversation around an issue and/or data.  This particular protocol that the teachers used is very effective in getting all the ideas out “on the table,” removing the ideas that are out of their control, categorizing the ideas, and then finally, choosing the top three.

In this training, teachers were working in the Problem Solving Process (Response to Instruction and Intervention) to determine what was causing the problem (hypothesis) they had earlier identified by analyzing various data reports. Once they had brainstormed their ideas about why the problem was occurring and isolated the ideas that they knew they could not change, they categorized their ideas  under four domain headings: Instruction, Curriculum, Environment, and Learner (ICE + L). These domains reflect “where the hypotheses live.” Categorizing this way, helps teachers to be more targeted in their intervention design.  When trying to determine why the problem is happening, we encourage teachers to look at the other domains–Instruction,Curriculum,Environment–before stating the problem is with the Learner.  Yesterday in the training, John commented that if you put the “L” first you have lice; therefore, if you go to the Learner first, you will have “lice.” The third poster illustrates John’s idea. Of course, the hypotheses they eventually chose, did not focus the problem on the learner, so they avoided the lice problem.

Before this training I had never used the Affinity Protocol. Now I am a total convert. After introducing and explaining it to the teachers, they started working. I was a little worried in the beginning because they didn’t seem to be brainstorming as quickly as I expected; however, my worrying was premature. Within five minutes the conversations started, the post-its started multiplying, and they were fully engaged with the task. My partner and I just took pictures, listened in on their conversations, answered question,s and made comments when asked. It was a facilitator’s dream!

Data Metaphors or Similes

Using metaphors as a teaching strategy should not be limited to those of us  in the  language arts. Creating metaphors or similes enables students to think about concepts, people, history, etc., in a variety of different ways.

I would love to read your “data metaphors.” How would you describe data metaphorically? What would you use as a comparison?

Here are a couple of my attempts:

Metaphor: Data are a cacophony of notes waiting for the composer to shape their melody.

Simile: Data are like  trails of breadcrumbs that sometimes leads us to places we are unfamiliar.

Analyzing metaphors is much easier for me than writing them. I hope you take on the challenge.

 

Do I really have to collaborate?

                                       “Meaning and action result from collective processes that develop shared commitment to improved student learning.”
–Data-Driven Dialogue

I started my career in education as a high school English teacher. Collaborating in high school is generally a struggle. Time–the lack of it–is one of the primary factors, but also high school teachers have become comfortable staying behind “closed doors.” However, times are changing:  more and more teachers (elementary and secondary) are opening their classroom doors (willingly or with a gentle tug)  and collaborating to find the most effective ways to meet the academic and behavior needs of their students.

When analyzing student data (academic, attendance, or behavior), the research and my own personal experience, proves that collaboration is best. But why?  In the book, Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning, Kathryn Parker Boudett and Liane Moody state:

When people are involved in analyzing and interpreting data collaboratively, they become more invested in the school improvement efforts that are generated out of those discussions. The more people involved in data analysis and interpretation, the more effective the resulting school improvement efforts will be.

We cannot  improve our schools if only a few people are holding the keys. Teachers must be involved in the data inquiry and problem solving process in order to bring authentic and effective instruction into their classroom. Teachers must have a voice in this process because their knowledge and understanding of their students are critical components of the data analysis ( Even though I am primarily focusing on teachers in this entry, it is  important to remember that all other stakeholders–clerical, aids, parents, students–are involved in the data inquiry and problem solving process at various times and for various purposes).

When working with more than two people, especially if a team is just beginning to work with data, it is important to have structures and protocols in place to effectively guide the conversations (more on data protocols in upcoming entries).  My first experiences on a school data team made me appreciate having some type of structure to follow. Working with data can be very overwhelming; you can literally get lost in the data, especially when working with team. Structure and protocols, when consistently used, help prevent getting lost in the data labyrinth.

I hope to revisit in future entries. However, before I sign off, I have to make a Star Trek analogy (you thought I wouldn’t do it). Captain Jean Luc-Picard, the captain of the Starship Enterprise, explored the galaxy with his crew–his team. Working with Data (sorry, I couldn’t resist), and the other members of his senior team, he traversed the galaxy, battled the Borg, and had unending adventures (inlcuding my favorites with Q). Star Trek–all five series–are driven by a team of characters. They come together in each show to save worlds, their ship, and each other, from continual disasters. We as educators have to come together, work collaboratively, to save our students from falling into the black hole of low achievement.