| Matthew Chen | 41 | 2 | 2 | Can't explain what the Fourier transform actually does; LO3 not attempted |
| Sarah Kim | 46.5 | 2 | 2 | Frequency domain unclear; confusion matrix only with heavy scaffolding |
| James Wilson | 64 | 3 | 3 | Identifies sonogram axes with prompting; precision/recall vague |
| Marcus Brennan | 68 | 5 | 2 | Strong LO1, but confuses precision vs. recall |
| Elena Vasquez | 70 | 3 | 5 | LO2 jumped in session 2; sonogram→ML bridge still incomplete |
| Devon Carter | 66.5 | 3 | 3 | Two sessions, no improvement; LO2 regressed on FP vs FN |
| Aisha Mohammed | 72 | 3 | 5 | Confuses deployment with testing; sonogram→ML bridge weak |
| Priya Raman | 65 | 4 | 2 | Keeps mixing up false positives and false negatives across both sessions |
| Tyrone Jackson | 74 | 3 | 5 | Confused brightness with frequency on sonogram axes |
| Leo Fitzgerald | 69 | 3 | 3 | Strong LO3, but reversed FP/FN definitions and weak on sonogram purpose |
| Hazel Andersen | 71.5 | 3 | 5 | Can't explain why a 2D image of audio enables image-based ML |
| Liam O'Connor | 88 | 5 | 4 | Mastery across all three learning objectives |
| Nora Bellamy | 91.5 | 4 | 5 | Strong sonogram and confusion-matrix reasoning |
| Kai Nakamura | 78 | 5 | 4 | Clear on FFT, classification, and deployment vs training |