Is your teaching Data-Informed?

How many schools focus only on the collection of data from standardized test scores or benchmarks? Well, that is only one piece of information. I recently started a data course and it is so amazing! What good is learning if you are not sharing with others? So I challenge you to reflect on your own data collection and answer the following question:

 

 

Are you data-literate and is your teaching data-driven?

 

 

 

 

Being data literate is an action or habit of the mind, not just knowing about data. It is not about compliance, but about leadership. It means you have the ability to gather, interpret, and use multiple data sources effectively to improve student learning. We can not become a data-driven teacher without building our data literacy. As you learn about data you must share with your colleagues. Teach them how to become data literate, not just about data. In other words, you must not teach them how to “eat,” but teach them how to “cook.”

4 Types of Data

1. Student Learning Data– This is the data you collect that shows you what your students are learning. This data should tell you how much students are learning, if they are learning on high levels, and if the students’ learning is aligned with the learning outcomes.

Examples of collection of this type of data: Quizzes, tests, benchmarks, self-reflections, observations, class discussions, student/parent conferences, journals, universal screenings, etc.

2. Demographic Data– Collection of this type of data tells you about student interest, student home life, who encourages the student’s learning outside of school, and what the family access to technology outside of school is. You may gather information that tells you what the reading level at the student’s home is, what the community is like, what the community’s perception of the school and teachers is, and what the community’s needs are.

Examples of collection of this type of data: Student surveys, posing questions, parent contact, information sheets, twitter, phone calls, decorations from students, google, newspaper, Facebook, gossip, etc. 

3. Perception data- This data is often more important than reality. “What we think students are thinking is not generally what students are learning.” This data will tell you if they believe you have their best interest at heart, what is most important to them in the school day, and what they believe reading and writing is. You can ask students what they think about the class and if the lesson is too hard or too easy? This data will tell you if the students think they can achieve their goals. It is important to know the importance of gathering student perception data.

Examples of collection of this type of data: Asking students questions, reflection questions, twitter comments, emails to parents, surveys, students reflections, suggestion boxes, discussion with alumni, phone calls, etc.

4. Process data- This data will tell you what happened in the learning environment. Did you make them think at high levels? Was your instruction effective? Did the students learn?

Examples of collection of this type of data: Evaluations from observations, student reflections, journals, conversations with other teachers, reflections, students conferences, discussions with students, common assessment reflection, peer observations, visiting other schools, conferences, comparing to other schools, videos, etc.

 

 

 

 

If you want to become data-driven you must use all types of data. When you limit the data, you limit your effectiveness. Data driven instruction cannot be driven by one piece of data. You must be data-informed and data empowered! 

If you collected student learning data and know you have X student that is 5 points away from passing his reading EOG score, you cannot do much with that data. If you collect the perception data that student X thinks reading is boring and does not like to read, you cannot do much with that data. If you collect process data that tells you the reading material was too hard for the student you cannot do much with that data. However, when you collect the last piece of data, demographic, and you have all four pieces of data you can do UNREAL things! The demographic data tells you the student likes soccer. You can now adjust your teaching from the data! Your data-driven decision may be to change the reading material to soccer related material, pair the student in class with someone who can help them with the harder reading material, teach using a “hook” to get the student interested in reading, and look at the student learning data as only piece of information.

So, are you data-literate and is your teaching data-driven? If you answered no, what will you do to become data-empowered? If you answered yes, how will you teach your colleagues how to cook?

*Credit learning of this material through NCAAT Data Literacy Course and through instruction of Jennifer Morrison.*

 

 

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One thought on “Is your teaching Data-Informed?

  1. I love these categories / descriptors for types of data. I’d change the analogy about cooking, though. I agree that we need to teach kids “how to cook,” but it seems to me that we’re not even teaching them “how to eat.” Instead, so much of what we’re doing equates to seeing “how much they’ve eaten” without ever even considering where the food’s come from or if it’s at all nutritious.

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