Evidence-Informed Decision Making
“How do I use various forms of evidence—and recognize their limits—to inform my teaching?”
Only 17% of teachers report receiving data training during their preparation programs, despite 77% saying their school leaders encourage them to use data (EdWeek/Data Science 4 Everyone, 2025). A 2025 scoping review of 276 studies confirmed persistent confusion between assessment literacy and data literacy, with most training prioritising analysis over instructional application. This pathway closes that gap — teaching teachers not just to read data, but to ask the right questions, recognise the limits of evidence, and translate findings into next-day teaching decisions.
6 series
1 foundation · 3 applied · 2 electives
Applied Classroom Analytics
Your Learning Path
The course is composed of the following series. Complete each and personalize your learning by choosing an elective. When you are ready, submit your capstone and coursework to earn your certificate.
F
— Timeless knowledge that grounds everything
Data Science Foundation for Teachers
Build foundational data literacy — understand what data is, what types exist in schools, how to read visualizations, and how to ask the right questions of data before jumping to conclusions.
A
— Put foundations into practice and apply what you've learned in your own classroom.
From Data to Decisions
Apply a practical data-to-action cycle: identify the question → choose the right data → analyze for patterns → make an instructional decision → check if it worked. Master the 'same-day data loop' and the 'small data revolution.'
Leveraging Data in the Classroom
Applications of data science for the classroom - move from data overload to sustainable data analysis habits for teaching by developing workflows for data analysis and response.
Data Ethics, Equity, and Stories
Examine how data can perpetuate or disrupt inequity — understand bias in assessment design, the danger of deficit narratives, disaggregation as a justice tool, and the ethical obligations of holding student data.
E
— You will pick 1 elective(s) that best matches your context and interests!
Data Visualizations and Communication
Create clear, honest, and compelling data visualizations — for parent conferences, PLCs, admin reports, and student self-assessment — that tell accurate stories and drive productive conversations.
Introduction to SEL Data
Understand how to collect, interpret, and use social-emotional learning data — including climate surveys, SEL assessments, behavioral data, and student self-reports — to inform whole-child teaching decisions without reducing students to scores.
Capstone & certificate
Capstone reflection
Review your artifacts, notice patterns in your growth, and describe where you started, what you practiced, and where you are headed.
Where I started
What I learned through practice
Where I'm going
Your certificate includes
Certificate title: Applied Classroom Analytics
Documented learning and application time
Series completed with competency statements
Alignment with professional teaching standards
A shareable record of your work
Portfolio — cover page, competency map, artifacts, and capstone reflection together describe what you did and what changed in your practice.
Applied Classroom Analytics
A portfolio-friendly record of your work across our Evaluating AI in Education course.
Build understanding through snacks, materials, and mini-experiments across each series.
Document classroom try-outs and reflections as artifacts you can reuse in a PLC.
Connect research to practice with structured prompts in every learning series.
Earn a certificate title backed by documented hours and completed series.
See how foundations, applied work, and electives add up to one coherent story.
Assemble portfolio pieces—competency map, artifacts, and capstone—for district conversations.
