AAQEP Standard 1: collecting evidence that actually shows candidate competence

AAQEP Standard 1 asks a deviously simple question: can your completers do the work of teaching well, in the contexts where they'll actually teach? The standard names four aspects of candidate performance: content and pedagogical knowledge, learner-centered practice, professional responsibility, and adaptability across diverse contexts. Your evidence must speak to all four, not just the ones that are easiest to measure.

Start with the four aspects, not your existing data

The most common mistake is opening a spreadsheet of data you already collect and trying to map it backward to the standard. Reverse the direction. For each of the four aspects, write one sentence describing what a competent completer looks like in your program's context. Only then ask which existing evidence supports that picture, along with what gaps you need to fill.

The evidence reviewers expect to see:

• A performance-based assessment scored against clear criteria, ideally tied to InTASC or your state's professional standards, with rubric language candidates and faculty consistently and systematically use.
• Multiple data points per candidate across the program, not one capstone score, but a trajectory that shows growth and identifies struggling candidates early.
• Employer and completer perspectives, gathered systematically (not just anecdotally), with response rates and methodology disclosed.
• Evidence of candidate work with diverse learners, such as videos, lesson artifacts, or supervised practice documentation that shows performance in varied contexts.
• An analytic narrative that describes what the data shows, what it doesn't show yet, and what you plan on doing about both.

Make the case for validity and reliability

AAQEP doesn't require psychometric validation of every instrument, but it does expect you to demonstrate that your assessments measure what you claim they measure and that scoring is consistent across raters. Document calibration sessions. Report inter-rater agreement where you have it. If your sample is small, say so plainly, as reviewers respect honesty about sample size more than they respect protected language that hides it.

Disaggregate without overclaiming

Display performance broken out by each program, completer cohort or course, and where demographics permits. When a subgroup is too small to disaggregate responsibly, don't force the analysis. Instead, describe how you're building toward stronger data over time and what you're doing in the meantime to monitor equity in outcomes.

Organize the evidence room so reviewers can navigate it

• One folder per aspect of Standard 1, with a one-page index at the top explaining what's inside and why.
• Consistent file naming: instrument, cohort, date. Reviewers should not have to guess what they're looking at.
• An evidence map cross-referencing each claim in your narrative to the specific files that support it.

Close the loop in writing

The strongest Standard 1 submissions trace a visible thread: assessment → result → faculty interpretation → curricular or programmatic change → follow-up data showing whether the change worked. When the thread is incomplete, name it. Reviewers are far more skeptical of programs that present a perfect picture than of programs that show honest improvement work in progress.

Standard 1 evidence isn't a compliance process. Done well, it's the clearest mirror your program has for whether your candidates are ready. Build the evidence system you'd want even if no one were reviewing it; that's the system that will hold up when someone is.