Hidden Voices: Turning Student Climate Data into School-wide Change

Outline

Opening Story/Framing the Problem (8 minutes):
1a. Content:
Engagement: Kick off workshop with a scenario: Show responses from climate survey data. Set the stage. Ask participants to analyze and make direct connections to classroom practice. "Where in a classroom is this represented?" "Where in lessons might students need more scaffolding?"
Example: “In my class, I know how to keep trying even when the work is challenging."

Response  % of Students
Strongly agree  10 %
Agree  39 %
Disagree  36 %
Strongly disagree 15 %

Key Take Away:
Only 49 % of students agree they persist with challenging work, meaning less than half do not feel confident persevering.

Highlight the problem: Student voices provide tremendous insight into their own achievement data.

Driving question: How might we utilize our next climate survey to better explain our achievement data?

Mini-Lesson on *Research Findings (5-7 minutes):
*Research findings come from the presenter's original research and published dissertation on the topic. Three key findings will be presented:
Claim #1: There IS connection between Climate Data and Achievement data.
Claim #2: There are specific Climate survey items that give us windows into Hidden Beliefs About student Learning.
Claim #3: Traditional school improvement efforts often exclude students as stakeholders. Through adaptive leadership, leaders can draw out student perspectives to support teacher growth, align instruction with learner needs, and create the conditions where student attitudes fuel meaningful and sustained academic improvement.

Engagement: Digital audience poll: "Based on your experience and expertise, which of these findings most resonates with you?"

Content Part A: (10 min.)
Attendees will next be introduced to seven learning domains connected to schoolwide reading growth. Attendees will review how common climate survey items align with these domains, giving them a framework for interpreting student beliefs. This foundation will prepare participants to engage more deeply in the upcoming example data activity. The domains include:
- Adult Trust
-Cultural Diversity
- Responsible Decision-making
-Self-Awareness
-Goal Setting
-Self-Management
-Help Seeking

Interactive Activity: Turn and Talk - at first glance what do you notice? What surprises you? (5 min.)

Interactive Activity: Analyzing Sample Data ( 7 min.)
Attendees receive a set of REAL LIFE student climate data (modeled from dissertation’s anonymized descriptive statistics). A notetaker/one-pager categorizes survey items and data into the seven domains for attendees to reference later.

Process:
Individual review of sample data (3 min): Each participant scans the data, jotting initial notices/wonders.

Group discussion (5 min): Teams compare observations - What patterns emerge? Where might these show up in the classroom? Where are there red flags?

Content Part B: Designing Questions for Dialogue (10 min):
Using the group's analysis, participants design questions they could bring back to their own schools - for PLCs, staff meetings, or student focus groups.
Prompt: "If this were your school's data, what 2-3 questions would you ask staff or students to dig deeper?"

Closing Reflection (5 minutes):

6a. Content
Revisit driving question: "What might change if we actually listened to the hidden voices in our data?" Then emphasize urgency: Research indicates that elementary student learning beliefs calcify early - leaders must act now to cultivate belonging and connect learning to learners.

6b. Engagement
Individual reflection: Digitally share written reflection to the following prompt: "What's one step I will take with my next climate survey?"

Outcomes

Outcomes: After this session, school leaders will be able to…

Recognize how common survey items connect to key student success skills—like goal-setting, confidence, motivation, and reflection—and why these matter for achievement.

Spot patterns in survey data that reveal what students believe about learning, especially in relation to reading growth in Title I schools.

Understand how student attitudes shape engagement, ownership, and equity in learning.

Use a simple protocol to turn climate surveys from compliance reports into meaningful conversations with staff and students.

Plan one concrete step to bring student voice into school improvement efforts—building belonging, connecting learning to learners, and driving lasting growth

Supporting research

1. Alvi, E., & Gillies, R. M. (2020). Teachers’ perceptions of implementing self-regulated learning: “It’s all about scaffolding.” Frontiers in Education, 5, 117. https://doi.org/10.3389/feduc.2020.00117

2. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26. https://doi.org/10.1146/annurev.psych.52.1.1

3. Boekaerts, M. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231. https://doi.org/10.1111/j.1464-0597.2005.00205.x

4. Dignath, C., & Büttner, G. (2018). Teachers’ direct and indirect promotion of self-regulated learning in primary and secondary school mathematics classes—Insights from video-based classroom observations. Metacognition and Learning, 13(2), 127–157. https://doi.org/10.1007/s11409-018-9181-x

5. Heifetz, R. A., Grashow, A., & Linsky, M. (2009b). Leadership in a (permanent) crisis. Harvard Business Review, 87(7/8), 62–69.

6. Mitra, D. L. (2007). Student voice in school reform: From listening to leadership. In D. Thiessen & A. Cook-Sather (Eds.), International handbook of student experience in elementary and secondary school (pp. 727–744). Springer. https://doi.org/10.1007/1-4020-3367-2_29

7. Smith, R. A. (2024). The Influence of Student Voice and Self-Regulated Learning on State Testing Performance. https://scholarwolf.unr.edu/handle/11714/11026

8. Usher, E. L., & Schunk, D. H. (2018). Social cognitive theoretical perspective of self-regulation. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 19–35). Routledge. https://doi.org/10.4324/9781315697048-2

9. Vandevelde, S., Van Keer, H., & Rosseel, Y. (2017). Measuring the complex relations between student characteristics and self-regulated learning: A person-centered approach. Learning and Individual Differences, 54, 191–202. https://doi.org/10.1016/j.lindif.2017.01.001

10. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2