Your Weekly Digital Learning Compendium |
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Good morning, Vampires!
How many of us can confidently recall learning about data literacy in our teacher education programs?
Hop in to Learn and Grow Together with your Digital Learning Team, as we dig into a few resources about this often overlooked topic. |
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We completely get it: the daily demands of teaching leave us with little alternative when it comes to creating the time and space to learn new skills, or bolster dormant ones.
At the same time, evaluating data quality or knowing what inferences to draw from data is undeniably impactful for our teaching practice. |
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Make sense and better use of available data sets |
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| Platforms like Learning Analytics have vastly improved this process in recent years, by collecting information and providing us with powerful visuals, but the sheer quantity of data found there can leave some of us feeling overwhelmed. |
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A View of our Learning Analytics dashboard. |
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Data Scientists at McGraw Hill believe that having a strategy can instill confidence. Here’s a summarized version of their suggested approach:
1. Identify the types of data available:- Performance data measures student achievement and growth. Examples are MAP & F&P assessments, classwork, grades, GPA, graduation rates, and college acceptance.
- Other types of data, such as the information collected by PASS, illustrate students’ experiences at school.
2. Approach data with a curious mindset:
Ask questions that can bring clarity. Why is a particular set helpful in the first place? What are we trying to learn about this student/group?
3. Understand the data structure:- Volume: How many observations are there of the same analyzed item? “Confidence in a student’s skill level is higher based on an assessment with 30 questions than an assessment with 3 questions.”
- Frequency: How often are data collected and reported? “Collect data often enough to see growth over time and to identify opportunities to help students when they need it.”
4. Meaning and interpretation:- Accuracy: Do data sets consistently measure what they are supposed to measure? “Can the student correctly answer new questions that measure the same skill?”
- Completeness: How comprehensively does the data set capture information needed to reach a conclusion? “For example, teacher evaluations based on student test scores provide an incomplete picture of teacher effectiveness; qualitative information and other metrics designed for measuring effectiveness (e.g. student evaluations) would provide a more complete picture.”
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What’s available to us, at AISB? |
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Beyond the data available in our core Learning Management Systems (Seesaw, Classroom, Veracross and ManageBac), let’s zoom into Learning Analytics, currently available for grades 1 to 12.
The Student Data Profile, for example, is a great way to start. See this short tutorial to learn how to interact with it. |
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An Elementary Student’s Profile |
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| The MAP Explorer tool, for example, supports differentiation efforts with its helpful clustering of learners by readiness. You can even see skills students are ready for using the Descartes map. Watch this short tutorial. |
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MAP Explorer Tool overview |
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| Learning Analytics is a very powerful tool and has so much more on offer. Check out their Learning Center for more resources, and let us know if we can assist in any way by scheduling a one-to-one session with anyone from the Digital Learning Team! |
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MORE PROFESSIONAL LEARNING |
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Talking about data, have you seen the amazing content that former Science teacher Luke Scholtes is publishing to help students develop critical thinking skills with a Science lens? If not, take a look at this awesome sample below and head to his Youtube channel to subscribe for more.
Once a vampire, always a vampire! |
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| You don’t need to be a data geek to appreciate a Google Sheet shortcut. Learn to use the Explore Tool to make your life so much easier! Introduce a data set into Sheets, then select what you need and click Explore at the bottom. |
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