Using Data to Drive Instruction

Using Data

Pictured: The Norfolk 17

to drive school change

Tanishia Lavette Williams, PhD Brandeis University | Fall 2025

In February 1959, seventeen Black students integrated six all-white middle and high schools in Norfolk, Virginia, after a protracted battle against Massive Resistance policies that had closed schools to avoid desegregation. These students, later known as the Norfolk 17, faced daily harassment, hostility, and isolation, yet their courage marked a pivotal moment in the struggle to uphold Brown v. Board of Education and advance educational equity. Their presence in classrooms symbolized both the power of youth in driving change and the role of data—attendance, test scores, and enrollment numbers—in shaping debates over who had access to education.

Published in 1966, the Coleman Report (Equality of Educational Opportunity) marked a turning point in how data was used to define, and attempt to solve, educational inequity. Commissioned by the Civil Rights Act of 1964, the report compiled responses from over 600,000 students and teachers in nearly 4,000 schools across the United States. Its sweeping statistical analyses concluded that a student’s family background and peer group had more influence on academic outcomes than school resources themselves—a finding that sparked both controversy and decades of policy experimentation. While often cited for de-emphasizing the role of schools, the report also introduced quantitative measures of racial disparities in achievement, funding, and facilities, laying the groundwork for future accountability systems. As a data artifact, the Coleman Report is not just a study; it is a reflection of the questions a nation chose to ask, the limits of what was measured, and the policies those numbers were used to justify.

While studying in Germany in the early 1890s, W.E.B. Du Bois developed a series of intricate, hand-drawn diagrams exploring the structures and stratifications of knowledge. These hierarchies of learning—sketched in pencil and ink—offered an early glimpse into Du Bois’s lifelong interrogation of education as both a vehicle for liberation and a site of exclusion.

Course Description Using Data to Drive School Change invites students to interrogate the ways data can both illuminate and obscure the realities of teaching, learning, and schooling.

Positioned at the intersection of educational practice, policy, and equity, this course equips emerging teacherleaders with the analytical tools, reflective dispositions, and collaborative protocols necessary to make data a catalyst for meaningful change.

Rather than treating data as neutral, students will critically examine the social, political, and cultural contexts in which data is collected, analyzed, and acted upon. Through case studies, protocols for looking at student work, and engagement with multiple forms of evidence, including standardized test scores to qualitative narratives, students will practice translating data into insights that advance student learning and strengthen instructional practice.

At the same time, the course challenges participants to consider how data has historically been used to reinforce inequities and how teacher-leaders might reframe its use to build inclusive, culturally responsive, and justiceoriented school communities.

By the end of the course, students will have cultivated a deeper understanding of how to lead with data, not as a tool of compliance, but as a vehicle for reflection, collaboration, and transformation in their schools.

Develop Data Literacy for Leadership Build foundational skills in collecting, analyzing, and interpreting multiple forms of data (including student work, assessments, and qualitative evidence) in order to make informed decisions that strengthen teaching and learning.

Interrogate Power and Positionality in Data Examine how race, class, gender, and other dimensions of identity influence both the production and use of data in schools. Question how data has historically been weaponized against marginalized communities and explore more just alternatives.

Strengthen Collaborative Practices Engage in Professional Learning Community (PLC) protocols and other collaborative routines to use data as a tool for inquiry, reflection, and collective improvement rather than compliance.

Apply Data Across Levels of School Change Analyze how data functions at multiple levels (classroom, school, district, and policy) and how teacher-leaders can strategically leverage evidence to impact student learning and organizational culture.

Cultivate Culturally Responsive Data Practices Learn to frame, collect, and interpret data in ways that are sensitive to cultural context and reflective of the lived experiences of students and families.

Develop Reflective and Critical Thinking Skills Practice turning raw information into meaningful insights through cycles of inquiry, case study analysis, and critical reflection.

Learning Goals

Design for Equity and Transformation Translate data insights into action plans that address inequities, promote culturally responsive pedagogy, and foster more inclusive and just school environments.

Enhance Communication and Facilitation Skills Strengthen the ability to lead data conversations, present findings clearly, and facilitate difficult dialogues with colleagues around evidence, equity, and change.

Prepare for Real-World Application Equip yourself with practical strategies to use data as a lever for continuous improvement and to advocate for school policies and practices that support all learners.

Academic Policies Academic Honesty You are expected to be familiar with, and to follow, the University’s policies on academic integrity. You are expected to be honest in all of your academic work. Please consult Brandeis University Rights and Responsibilities for all policies and procedures related to academic integrity. Allegations of academic dishonesty will be forwarded to Student Rights and Community Standards. Sanctions for academic dishonesty can include failing grades and/or suspension from the university. Citation and research assistance can be found on the university library website.

Accommodations Brandeis seeks to create a learning environment that is welcoming and inclusive of all students, and I want to support you in your learning. If you think you may require disability accommodations, you will need to work with Student Accessibility Support (SAS). You can contact them at 781-736-3470, email them at access@brandeis.edu, or visit the Student Accessibility Support home page. You can find helpful student FAQs and other resources on the SAS website, including guidance on how to know whether you might be eligible for support from SAS. If you already have an accommodation letter from SAS, please provide me with a copy as soon as you can so that I can ensure effective implementation of accommodations for this class. In order to coordinate exam accommodations, ideally, you should provide the accommodation letter at least 48 hours before an exam.

Learning Expectations I recognize that students in this program are working professionals balancing multiple responsibilities. This course has been designed with intentional spacing, a week between most sessions, to provide time for reading, synthesis, and reflection away from class meetings. To support balance with professional responsibilities, students are expected to use the time between sessions for readings, written assignments, preparation for discussions, and other course activities.

Student Hours and Communication Held virtually, student hours occur by appointment. Students are encouraged to email the professor with any questions or if they cannot find a suitable time to meet using the provided link.

Attendance and Lateness Policy Class activities are designed to be completed in real time. Absence from class results in an inability to contribute to class discussion and a reduction in participation points. Unavoidable, emergency, unforeseen, and scheduled class absences should be discussed with the professor to determine suitable arrangements for recouping participation points.

Student Support Brandeis University is committed to supporting all our students so they can thrive. If a student, faculty, or staff member wants to learn more about support resources, the Support at Brandeis webpage offers a comprehensive list that includes these staff colleagues you can consult.

Course Materials The course readings include a diverse range of multimedia sources such as journal articles, research working papers, policy briefs, book chapters, and news articles. To ensure that all students can access the required materials without financial burden and to promote a fair and inclusive learning environment, links are provided to all mandated course resources.

Library Services The Brandeis Library collections and staff offer resources and services to support Brandeis students, faculty, and staff. Librarians and Specialists from Research & Instructional Services, Public Services, Archives & Special Collections, Sound & Image Media Studios, MakerLab, AutomationLab, and Digital Scholarship Lab are available to help you through consultations and workshops.

Religious Observance Policy The professor strives to support students’ observance of their traditions by allowing absence from classes for such purposes by endeavoring to ensure that examinations, written reports, oral reports, or other mandatory class assignments are not scheduled for or due on holy days and by providing ample opportunities for students to make up work missed on such occasions, without penalty. Should a student need to miss class for religious reasons, the absence will be excused. Classes missed for travel plans are not considered excused absences. Only the dates of the holidays themselves are considered excused absences.

Students should communicate any religious observances at the start of the class.

Statement on Respectful Environment Brandeis University is committed to providing its students, faculty and staff with an environment conducive to learning and working, where all people are treated with respect and dignity. Please refrain from any behavior toward members of our Brandeis community, including students, faculty, staff, and guests, that intimidates, threatens, harasses, or bullies. Please consult Brandeis University Rights and Responsibilities for all policies and practices related to a respectful environment.

Course Policies

Attendance and Participation Seminar courses require consistent attendance. Students are expected to come to each class prepared to contribute to the collective synthesis of ideas and discourse. At a minimum, students should complete the weekly readings and be ready to discuss a relevant example or student artifact that connects to the themes of the session.

Reading Acknowledgments This course provides students with a wide range of resources to deepen their understanding of the purposes and politics of data in education, drawing on multiple disciplines and racial equity perspectives. Each week’s readings include both anchor texts (required for all students) and additional materials from which students may choose based on their interests and contexts.

At the start of each class, students will complete a brief reading acknowledgment indicating which readings they engaged with and providing feedback on their usefulness. In addition, students will be asked to submit a short summary of a student artifact from their own practice or context that connects to the readings, highlighting how theory and practice intersect.

Session Discussion Canvassing and Engagement Each week, we will synthesize the assigned readings by putting them into conversation with one another and with contemporary media. Together, we will use current events, cultural texts, and school-based examples to connect theory and data from the readings to lived realities.

Students are expected to engage actively in conversation with their peers and extend these insights to the broader digital community each week. This practice will serve not only as a collaborative note-taking mechanism but also as a way to synthesize our collective understanding and connect our learning to the communities in which we are situated.

Evaluative Elements

Session Applied Assignments Each week, students will complete a short applied assignment aligned to the session theme. These tasks are designed to bridge theory and practice, requiring students to engage in pre-work before class (e.g., gathering materials, conducting brief observations or interviews, reviewing artifacts from their schools).

Students should come prepared to share their assignments in class as part of our collaborative inquiry.

The weekly assignments also serve as building blocks for the final Data Action Plan, providing artifacts, analyses, and reflections that can be incorporated into the culminating project.

Data Action Profile For the culminating assignment, each student will develop a Data Action Profile that demonstrates their ability to engage critically with data across multiple levels of education. The profile will be organized into six Lenses of Inquiry, each highlighting a different perspective on data: Identity Lens – personal reflection and self-generated data Instruction Lens – classroom-level teaching and learning data Student Lens – individual student voice, work, and outcomes Community Lens – school-level culture, climate, and organizational data Policy Lens – state-level policy, assessment, or demographic data Systems Lens – federal or national-level policy and comparative data In each lens, students will examine both a quantitative and a qualitative form of data, considering what the data reveal, what they obscure, and how they might be used to drive action. Many components will be drafted and workshopped during class sessions to ensure scaffolding and feedback.

The completed profile should illustrate not only a command of data analysis but also thoughtful reflection on how data shapes decision-making, influences equity, and impacts the lived experiences of students and educators.

Grading Structure Students will work toward a combined total of 2000 achievable points for all activities and assignments in this course. After each class session, students will be able to track their point accumulation.

Class Session Participation & Applied Assignments Across the 7 class sessions, students can earn up to 1400 total points. Each session includes a combination of pre-class preparation and in-class engagement, but the exact point distribution may vary based on the demands of the week’s content and assignments.

Data Action Profile | Due December 12 at 9PM (ET) Students will synthesize course learning into a comprehensive Data Action Profile that applies equitycentered data practices to a problem of practice in their own school or district. The plan should integrate insights from weekly applied assignments, DIG discussions, and personal reflections.

Cohort Norms Assume best and positive intentions, with a focus on empowering peers and inviting all to collaborate (commuters and residential).

Come prepared with pre-work completed, necessary materials, and remove distractions to respect others’ time and use it effectively.

Create space for the experiences, opinions, and skill sets of others Open communication Listening to understand not to respond Disagree while maintaining an inquiry stance (enter from a place of curiosity rather than judgment) Supportively challenge each other to move “one notch further” in our learning

Acknowledge that people engage, participate, and learn differently.

“Share the air.”

Have fun, keep it interesting, and take it easy!

Be mindful of these norms in all spaces.

Session One

Framing Data for School Change Histories, Possibilities, and Pitfalls

Objective: By the end of this session, students will be able to articulate multiple definitions of “data” in education, critically examine historical uses and misuses of data through an equity and justice lens, and reflect on their own experiences with data in schooling in order to surface assumptions, questions, and commitments that will guide their learning throughout the course.

Readings Gordon, N., & Conaway, C. (2020). Common-sense evidence: The Education Leader's guide to using data and research.

Harvard Education Press. Introduction.

Brookfield, S. (2017). Becoming a Critically Reflective Teacher. Chapter 1.

Ladson-Billings, G. (2006). From the Achievement Gap to the Education Debt: Understanding Achievement in U.S. Schools. Educational Researcher, 35(7), 3–12.

Reading Acknowledgment

Brookfield, S. (2017). Becoming a Critically Reflective Teacher. Chapter 4.

STANDARDIZED SELFIE STORY For this session, you will create a Standardized Selfie Story. This is a short reflective snapshot of your most vivid memory of taking a standardized assessment. Think back to the circumstances surrounding that experience: How did you prepare for the test? What was the environment like on the day of the assessment— classroom setup, teacher presence, peer energy, even your own emotions? Finally, recall to the best of your ability how you performed, and how you interpreted that performance at the time. The goal is not to produce a polished essay, but to capture the personal, human side of standardized testing—what it felt like to be you in that moment, and how that experience shaped your relationship with assessments.

Data Inquiry Groups (DIGs) will serve as your collaborative home base throughout the course. These small groups are designed to give you a consistent community of peers with whom you will practice structured protocols for analyzing and interpreting data. Each group will meet at least four times during the semester, using these sessions to rehearse and refine inquiry routines, test ideas, and support one another’s thinking. DIGs provide space for students to wrestle with evidence, surface assumptions, and apply identity and equity lenses to real problems of practice. By returning to the same group across multiple sessions, you will develop trust, sharpen your inquiry skills, and learn how to engage productively in cycles of collaborative data analysis.

READING DISCUSSION

Partner A: Summarize one core idea from Gordon & Conaway (e.g., limits of “evidence-based” practice, value of multiple data sources, avoiding bias).

Partner B: Summarize one core idea from Brookfield (e.g., assumptions in teaching, the four reflective lenses, power/hegemony in classrooms)

Assumptions & Evidence: How might unexamined assumptions (Brookfield) shape the way educators select, interpret, or dismiss evidence (Gordon & Conaway)?

Power & Data: Brookfield stresses examining power dynamics. How might power show up in what “counts” as evidence in education?

Practice vs. Research: Where do you see tensions or alignments between reflective teaching and evidencebased decision-making?

Choose ONE of the following quick scenarios and discuss: A district mandates a new reading program based on a “gold-standard” RCT study. How might Brookfield’s reflection help leaders interpret or adapt this evidence responsibly?

A teacher notices that students resist discussionbased learning, despite “evidence” showing its effectiveness. How would Gordon & Conaway’s broader view of evidence help in responding?

Data is information we collect to make sense of the world.

Data can be numbers, words, images, or observations.

It’s always shaped by purpose and context.

Evidence is not ju st “one study.” It is a body of information sh aped by values, perceptions, and constraints.

Leaders already use data, but often unintentionally, shaped by their own cognitive biases. We notice what affirms our beliefs and dismiss what doesn’t.

Our teaching and leadersh ip are always shape d by assump tions: paradigmatic , prescriptive , and causal.

Critical reflection requires intentionally checking those assumptions against lenses: students’ eyes, colleagues, personal experience, theory/research.

Data does not speak for itself. How we interpret data depends on our identities, experiences, and assumptions. Gordon & Conaway remind us that evidence is shaped by context and values, while Brookfield asks us to critically reflect on our assumptions and power.

Data Action Profile

What information do you trust most, and why?

What assumptions shape the way I interpret data about myself and others?

Brookfield’s Four Lenses

Students’ Eyes: Collect feedback or recall how students responded.

What surprised you, or didn’t match your intentions?

Colleagues’ Perceptions: Ask trusted peers to observe or critique your decisions. They’ll often notice assumptions you take for granted.

Personal Experience: Reflect on your own history as a student — how does it shape what feels “normal” in teaching or data?

Theory & Research: Read scholarship that frames familiar practices differently (e.g., Freire on dialogue, Ladson-Billings on culturally relevant pedagogy).

What are ways to collect this information?

Students’ Eyes: Collect feedback or recall how students responded.

What surprised you, or didn’t match your intentions?

Colleagues’ Perceptions: Ask trusted peers to observe or critique your decisions. They’ll often notice assumptions you take for granted.

Personal Experience: Reflect on your own history as a student — how does it shape what feels “normal” in teaching or data?

Theory & Research: Read scholarship that frames familiar practices differently (e.g., Freire on dialogue, Ladson-Billings on culturally relevant pedagogy).

Giorgia Lupi

Data in the Everyday

the small details of school life

Data in the Work

artifacts of learning (student writing, classroom displays, teacher notes, portfolios).

Data in the Story

qualitative accounts (student reflections, interviews, family voices, teacher observations).

Data in the Person

how identity markers (race, gender, language, disability, socioeconomic status) shape and are shaped by records.

Data in the System

aggregated, standardized information (test scores, graduation rates, accountability dashboards).

Refers to the raw stuff: individual pieces of information, observations, or records. Examples include one student’s attendance slip, a quiz score, a single survey response, a teacher’s note after class.

d

The Little

D

The Big

Refers to the system, narrative, or dataset built from assembling and interpreting little d data.

Examples include district-level attendance reports, state test score databases, federal datasets like NAEP. Big D is often what’s presented as “official” or “capital-D Data” in policy conversations.

Lupi, G., & Posavec, S. (2018). Observe, collect, draw!: A visual journal: Discover the patterns in your everyday life. Princeton Architectural Press.

Why the “d” Distinction Matters

Little d reminds us that data is human, contextual, and situated in lived experience.

Big D shows how institutions aggregate and standardize those pieces into powerful categories (achievement gaps, accountability ratings, etc.).

When moving from little d → Big D, whose voices and experiences are lost, and whose are amplified?

Data is never just numbers on a page.

Data are memories, stories, and perspectives that shape how we understand learning and ourselves.

As we begin this course, remember that every piece of data carries a lens: it tells us not only about students and schools, but also about the values and assumptions of the people who collect and interpret it. Our work this semester is to practice looking through those lenses with greater clarity, care, and courage.

Final Thoughts that came up in class...

Data Literacy Foundations: From Raw Information to Meaningful Evidence

2

Session

Bernhardt, V. (2013). Using data to improve student learning in elementary school. Routledge. (Chapter 2)

Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice. Teachers College Press. Chapters 3 and 7.

Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice.

Teachers College Press. Chapter 1.

Reading Acknowledgement

OBJECTIVE

By the end of this session, students will be able to identify and differentiate types of educational data (quantitative, qualitative, observational, student work, and instructional materials), evaluate the principles of validity and reliability, and analyze lesson plans or textbooks as data sources to interpret how knowledge is represented and how these materials can serve as meaningful evidence of student learning.

Data Action Profile

Purpose of the Data Action Plan: The DAP is the throughline of the course. It’s how you’ll connect our weekly concepts to your own context.

Each week, you’ll add a new “layer” of data analysis (personal, classroom, school, state, federal) to build a full profile by the end.

Think of it as a portfolio of evidence that shows not just what data exists, but how you can use it for reflection, action, and change.

By the end, you’ll have something actionable: a story of data that highlights opportunities, challenges, and possible interventions.

The Identity Lens: Before we dive into datasets, we start with ourselves.

Each of us comes to this work with personal histories, identities, and experiences that shape how we see data.

The identity lens means asking: What aspects of my identity shape the way I view student performance, lesson plans, or test results?

Where might my experiences help me see nuance that others miss?

Where might my experiences create blind spots?

Using the identity lens is not about getting the “right” interpretation, but about being aware of how our perspectives influence what we notice, value, or question in the data.

Refers to the raw stuff: individual pieces of information, observations, or records. Examples include one student’s attendance slip, a quiz score, a single survey response, a teacher’s note after class.

d

The Little

D

The Big

Refers to the system, narrative, or dataset built from assembling and interpreting little d data.

Examples include district-level attendance reports, state test score databases, federal datasets like NAEP. Big D is often what’s presented as “official” or “capital-D Data” in policy conversations.

Quantitative Data

Shows trends, comparability.

Risks: flattens nuance, depends on measurement validity.

Examples: Test scores, graduation rates, and surveys with scaled responses.

data =/ truth

Qualitative Data

Captures experience and meaning.

Risks: subjective interpretation, harder to generalize.

Examples: Student reflections, teacher interviews, focus groups.

Common sources/methods for collecting data Observational data – classroom walkthrough notes, video of instruction.

Useful for describing context and dynamics.

Risks: shaped by observer’s perspective and bias.

Student work – essays, math solutions, art projects, digital products.

Evidence of thinking, process, and skill.

Risks: requires context (the prompt, scaffolds, instructions).

Instructional materials – lesson plans, textbooks, rubrics.

Reveal what knowledge is valued and how it’s represented.

Risks: show intended learning, which may differ from actual learning.

Clocks

is there a there there?

Public Transportation

is there a there there?

Thank You’s

is there a there there?

Mirrors

is there a there there?

The Utility of Lesson and Unit Plans

This textbook was widely used in Virginia History classrooms in the 1960s.

Session 3

Data in Action: Collaborative Inquiry and Professional Learning Communities

BEFORE YOU ANALYZE ANY DATA, YOU MUST FIRST TAKE AN HONEST LOOK AT YOURSELF.

Data Action Profile | The Identity Lens

Evidence is never neutral.

As Gordon & Conaway remind us...

Our race, gender, class, language, and lived experience shape what we notice and how we assign meaning.

ata d t e r p r e We int wn o r u o h g throu ons. i t p m u s s a

Brookfield argues...

The sources you believe are trustworthy

Your Experiences Impact

The assumptions that shape your interpretation

Data Action Profile | The Instructional Lens

Lesson and unit plans show what knowledge is prioritized, how learning is structured, and which students are imagined as the audience.

Examining them reveals both the intended curriculum and the assumptions within it.

What is a Collaborative Assessment Conference?

What is the Community Assessment Conference?

See what the work reveals about the

A structured way to look closely at student work together

Suspend judgment; notice before you evaluate

student and implications for teaching

Why this protocol? (3 Key Ideas) Student learning is layered. We often can learn more than the articulated standard.

When we suspend judgment and evaluation, we can see more layers to learning.

r Othe ves i t c e persp s see u p l he we what t migh rwise othe miss

Every piece of student work, no matter how routine, reveals something the student is trying to figure out, express, or master.

Choose a Facilitator

COLLABORATIVE ASSESSMENT CONFERENCE Start with a group of colleagues Collaborators observe and take notes on the sample

Presenter shares the work (no context)

Choose a Time Keeper

Collaborators ask questions about the work

Collaborators describe what they see, avoiding judgements

Presenter responds to questions and speculations

Presenter takes notes

Collaborators make speculations and provide suggestions

Presenter shares surprises Group Reflection

Gratitude

COLLABORATIVE ASSESSMENT CONFERENCE

Start with a group of colleagues Choose a Facilitator Choose a Time Keeper Presenter shares the work (no context) Collaborators observe and take notes on the sample Collaborators describe what they see, avoiding judgements Collaborators ask questions about the work Presenter takes notes Collaborators make speculations and provide suggestions Presenter responds to questions and speculations Presenter shares surprises Group Reflection Gratitude

Session 4

Data in Action: Collaborative Inquiry and Professional Learning Communities (Part 2)

Session Objective: By the end of this session, students will identify what makes a Collaborative Assessment Conference distinct and reflect on how the process can enhance teacher practice and student achievement.

Collaborative Assessment Conference (CAC): Seeing Students Through Their Work

Why this protocol Exists

To systematically look at student work without judgment To surface what the work reveals about a student’s thinking, strengths, and struggles To expand perspective through collective interpretation To use insights to refine instruction and deepen equity of understanding

Key Idea Students use assignments to tackle problems they care about (not always the ones we assigned).

What it Means Student work reveals curiosity, not just compliance.

Why it Matters Helps us locate student motivation.

We must suspend judgment long enough to see what’s really there.

Observation before evaluation.

Keeps us from projecting our expectations.

We need others’ perspectives to see what we’d otherwise miss.

The group creates multiple lenses.

Builds collective insight and equity.

Three Key Ideas

Choosing the Right Artifact: Seeing the Learner, Not the Lesson

Type of Work

What it Shows

What it Often Hides

Finished product (e.g., final essay, test, project)

Mastery, outcomes, adherence to task

Student reasoning, revisions, internal struggle

Draft / work-inprogress

Thought process, feedback loops, risk-taking

Creative / openended task

Student choice, identity, values

Final polish

Clear “right answers”

Highly structured worksheet / closed task

Procedural fluency

Conceptual depth

Multi-modal (audio, visual, performance)

Expression, emotion, nontextual intelligence

Traditional literacy measures

CAC Potential Use if annotated or if reflections are visible

Best for understanding how students think Excellent for probing motivation and interpretation Use selectively— pair with discussion notes or reflections

Expands equity in whose learning we see

Misaligned Student Work Samples

A Good Rule of Thumb Choose work that raises questions, not just scores answers.

Ask yourself: Does this artifact make me curious about how the student was thinking?

Could someone who doesn’t know my class still have something to say about it?

Can we see decision-making in the student’s process?

Is the artifact safe and ethical to share publicly?

Sweet Spot for CAC Strong CAC artifacts are: Open-ended: allow multiple valid approaches or interpretations Authentic: connect to real ideas or identities, not only school compliance Complex enough to wonder about: provoke curiosity, not certainty Visible thinking: include drafts, annotations, revisions, or reflections Safe to analyze: can be anonymized without harming dignity Situated: come from real classroom tasks, not artificial samples