Today鈥檚 guest post is written by frequent Finding Common Ground blogger Lisa Westman. Lisa is an instructional coach specializing in differentiation for Skokie School District 73.5 in suburban Chicago. She taught middle school gifted humanities, ELA, and SS for twelve years before becoming a coach.
鈥淚f we have data, let鈥檚 look at data. If all we have are opinions, let鈥檚 go with mine.鈥�
- l
If you want to get an educator鈥檚 attention, just say the word differentiation. Call me naive, but until last summer, I had no idea that this word provoked such a wide range of reactions from all education stakeholders.
Then, last August, I wrote my first guest blog post for , , and the floodgates opened. It turns out, people have strong feelings about differentiation, and I have been listening and gathering specifics on these views.
Since the post was published, I have written and presented about differentiation quite a few times. Most recently, I presented alongside at and co-moderated on the topic. Every time I write or present on differentiation, I note the questions and comments readers or participants make, and I have found some common themes in response to the topic of differentiation, such as:
- Teachers often feel unequipped to differentiate effectively.
- Administrators don鈥檛 always recognize differentiation when they see it, or they think they see 鈥渄ifferentiation鈥� when what they really see is 鈥渄ifferent鈥�.
- Many gifted education advocates believe the needs of gifted students cannot be met in the 鈥榬egular鈥� classroom through differentiation.
- There is a pervasive generalization and misunderstanding of the words: assessment and data.
Over time, I plan to address the first three bullet points in detail, but for the purposes of this post, I want to explore the fourth item- the words assessment and data. The misleading associations with these words (assessment=test, data=numbers) have become a giant barrier for teachers who strive to differentiate instruction yet struggle to do so effectively.
The Wicked Witch of The West Assess
I was never the best geometry student, but the one thing that stuck with me was 鈥�all squares are rectangles, but not all rectangles are squares.鈥� The same can be said about assessments, 鈥渁ll tests are assessments, but all not all assessments are tests.鈥�
Merriam-Webster dictionary defines assess as, 鈥�to make an approximate or tentative judgment鈥� and tests can certainly do this. However, often times tests are the least effective way to ascertain where students are and what they need. Test results amass a certain type of information and to differentiate successfully, other evaluations (observations, writing samples, conversations) and facets (social-emotional, aptitude, growth) of student performance must be considered.
The way we assess and the assessments we use give us the data we need to inform how to appropriately differentiate instruction for students. Therefore, if we are not using a variety of reliable assessments, our attempts to differentiate instruction often fall flat because the data we try to use doesn鈥檛 give us the information we need.
Follow The Yellow Brick Road Data
The word data does not have a warm connotation. Saying 鈥�data鈥� in conjunction with student learning often feels sterile and uncaring. I often hear sentiments like, 鈥�students are more than a number.鈥� And, when I presented with Carol Ann Tomlinson she responded to a question about using data with, 鈥�data sounds like something spit out by a machine.鈥�
And, I agree, students are more than a data point. They are more than a number spit out by a machine. And, so is data. Data is more than just numbers, and it can indeed be gathered and appraised in compassionate ways.
Let鈥檚 look at an analogous situation: a child鈥檚 visit to his pediatrician. When a child visits his doctor, he is more than a number there, too. Therefore, in order to form a diagnosis, pediatricians look at a variety of evidence, some which comes from a lab or machine (weight, temperature, blood count) and some which comes from other assessments (conversations, questionnaires, observing the patient perform a task). Yet, there is little complaint about using multiple types of data in a medical setting. In fact, I surmise that if a doctor made a diagnosis without various types of data, there would be quite a bit of protesting.
So, what is the difference?
In education, we seem to think that the only usable data we have are numbers: test scores, IQ scores, attendance rates, etc. This is like saying the only data a doctor can use is the patient鈥檚 height, weight, blood pressure, etc.
If this were the case, think of how many misdiagnoses would be made from only using these pieces of evidence? The doctor would not have some of the vital information (data) he needs to diagnosis the patient and prescribe a course of action.
Instead, doctors are also highly dependent on information that comes directly from the patient via conversations and observations. This is data which is collected with sensitivity and not calculated by an algorithm. A doctor uses information from all of these sources to differentiate his approach for his patients, so they thrive.
The same holds true for using data to differentiate for our students in the classroom. When we say the word data in education, we are simply referring to the different types of evidence we gather and consider to differentiate instruction for our students, so they thrive.
There鈥檚 No Place Like Home A Data Dashboard
In summary, differentiation is a natural byproduct of collecting and using the right information and the traditional methods of teacher data collection are quickly (if not already) obsolete.
Luckily, help is here. In Data Dashboards a High Priority in National Ed-Tech Plan, 澳门跑狗论坛 contributor Malia Herman states:
The push for wider and better use of data (dashboards)--which allow educators to examine and connect relevant student data from multiple sources--is growing stronger...learning dashboards integrate information from assessments, learning tools, educator observations, and other sources to provide compelling, comprehensive visual representations of student progress in real time."
To keep the analogy going, a data dashboard is like a patient鈥檚 chart at his doctor鈥檚 office. This is the place where all of the information is housed on individual students and their growth over time can be contemplated. And like a patient鈥檚 chart, only certain people are privy to individual student鈥檚 information. This comprehensive view of a student makes differentiating for their needs more accessible, attainable, and sustainable.
What are your thoughts? What is your experience using data to differentiate instruction? What successes and struggles have you encountered? Please comment below or tweet your response so we can learn from each other.
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