I learned that data alone doesn't fix problems. You have to dig into why the gaps exist, not just point at them. Root cause analysis is the step we skip, and that's where the real work happens.

I plan to use fishbone diagrams and the five whys with my team to investigate our equity gaps instead of just reporting them. I'll also align our PDSA cycles with our CLNA timeline so continuous improvement is built into the year, not an afterthought. One gap at a time. Start small. Fix what we can.

I learned that good data is useless if nobody can understand it. How you present data matters as much as what the data says. I plan to stop dumping spreadsheets and start building audience-specific dashboards and one-pagers. I'll add context to the numbers—what they mean and why they matter. Advisory boards get trends and gaps. Families get simple visuals. Translators for non-English speakers when needed. Focus on clarity, not quantity.

I learned that aggregate data hides more than it reveals. Breaking it down by gender, race, special populations, and program area exposes gaps we've been ignoring. The Ohio Equity Labs model was helpful—identify the largest gap, do root cause analysis, develop a SMART goal, and act.

I plan to run a disaggregated report on our program enrollment and completion rates by subgroup, identify the largest gap, then figure out why and build an action plan to close it. 

I learned that aggregate data is only as useful as the questions we ask of it. Validity and reliability matter, and descriptive statistics—distribution, central tendency, dispersion—give us a language for understanding what we're seeing. Multiple years of data smooths out outliers and reveals real trends.

I plan to spend more time looking at multi-year trends instead of reacting to single-year fluctuations. I also want to familiarize myself with our state's SLDS and use it to track program outcomes over time. And I need to be better about using aggregate data to advocate for program decisions—not just report what happened, but… >>>

I learned that FERPA, COPPA, and state laws set the rules, but best practices like secure storage, access control, and data minimization are what actually protect students. PII is broader than most people think.

I plan to review our current data practices, tighten vendor approvals, and make sure staff understand what's required. Also need to be more transparent with families about how their data is used. Simple, consistent, and necessary.

I learned that LMI isn't just for reporting; it's for making decisions. I've been using it to justify what I already do, not to challenge whether I should be doing it at all. The CLNA is supposed to be a tool for hard conversations, not a compliance exercise.

I also realized I'm not sharing LMI with students and families nearly enough. If they don't know what jobs exist and what they pay, they're making decisions in the dark.

I intend to use LMI more intentionally, to actually examine program relevance and not just confirm what I already believe. I'll also… >>>

I learned that quantitative data tells us what is happening, but qualitative data tells us why. The numbers alone don't explain barriers or student experience.

I'll use focus groups and surveys to dig deeper into our program gaps and triangulate findings with Perkins data before making changes.

This module gave me a clearer picture of what happens to data after I enter it. I've always known Perkins reporting happens, but I hadn't thought much about verification, validation, or the systems behind it.

The Texas automated coding system stood out—districts submit course records, the state codes students automatically, then sends it back for districts to confirm or flag errors. That's a level of accountability I hadn't considered.

I learned that we collect a lot of data but we rarely use it to interrogate our assumptions. The real value isn't in the numbers themselves. It's in what we're willing to learn from them, especially when they challenge us.

I plan to do a complete data inventory of our program, starting with disaggregated outcomes by subgroup. I want to find the uncomfortable truths (who we're serving well and who we're not) and build our improvement plan around that, not around what we already know works.

We keep treating work-based learning like a logistics problem—count hours, check boxes, call it success. The field's been doing this since 1976, when Congress mandated evaluation provisions that forced us to count everything except what actually matters. Dewey warned about "mis-educative" experiences—ones that arrest growth rather than advance it. A WBL placement that doesn't challenge a student, that treats them like a warm body in a chair, isn't just ineffective. It teaches them that work is hollow, that their time doesn't matter.

So I'm done performing accountability. I want to build a system where employer evaluations, student reflections, and skill… >>>