Speaking exposes gaps that silent recognition can hide.
The more legitimate learner and educator context available, the more useful the next action becomes.
SPEECH & SELF-EXPLANATION
The second performance matters more than the first.
HAOLLA’s learning model uses speaking, explanation and presentation as observable evidence of understanding. Automated video-level analysis is a product direction and should be introduced only where the feature is actually available.
HAOLLA METHODExplain → feedback → perform again
AI supports repeatable analysis while important judgment and learning ownership remain human.
Do not assume automated video analysis is live unless that feature is enabled in the current product.
HAOLLA avoids unverified claims of official status, guaranteed outcomes, affiliation or scoring authority.
HOW IT WORKS
Start small. Use the result to decide what comes next.
A single analysis matters only if revision and re-performance become useful learning signals.
출발 맥락을 명확히 하여 불필요하고 방향 없는 학습을 줄입니다.
목표를 관찰 가능한 행동으로 전환하여 유용한 학습 신호를 만듭니다.
학습량을 늘리기 전에 가장 영향력이 큰 변화부터 분리해 피드백합니다.
수정된 수행 결과를 다음 학습 설계와 판단을 위한 근거로 연결합니다.
HUMAN-CENTERED BY DESIGN
Learning ownership matters more than automation volume.
Learners revise and explain, educators retain important judgment, and AI helps with repeatable analysis and pattern detection.
✓Human override
✓Revision-first workflow
✓Context + longitudinal signals
FAQ
What to know before you start
Why use self-explanation?
Explaining forces the learner to organize relationships and reveals gaps that recognition alone can hide.
Is video analysis available now?
The platform direction includes video/speech analysis, but availability should be confirmed at the point of use.
Can this support presentation practice?
Yes. A strong workflow is first performance → focused feedback → second performance → comparison.