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Fully Adaptive Framework: Neural Computerized Adaptive Testing for Online Education

AAAI 2022 · Reinforcement learning › Visual and representation RL · approach: RL with tree search for discovery · 60 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use NCAT instead of BOBCATIRT-based CAT · ASSIST, step 20ACC: 0.749 -> 0.756Helped
Use NCAT instead of BOBCATIRT-based CAT · NIPS-EDU, step 20ACC: 0.723 -> 0.732Helped
Use NCAT instead of MAATIRT-based CAT · EXAM, step 20ACC: 0.845 -> 0.852Helped
Add Performance Learning to Contradiction Learning (ablation)NCAT · ASSIST, IRT, step 20ACC: 0.7415 -> 0.7562Helped
Add Performance Learning to Contradiction Learning (ablation)NCAT · NIPS-EDU, IRT, step 20ACC: 0.7059 -> 0.7321Helped
Add Performance Learning to Contradiction Learning (ablation)NCAT · EXAM, IRT, step 20ACC: 0.8351 -> 0.8516Helped
Add Contradiction Learning to Performance Learning (ablation)NCAT · ASSIST, IRT, step 20ACC: 0.7508 -> 0.7562Helped
Add Contradiction Learning to Performance Learning (ablation)NCAT · NIPS-EDU, IRT, step 20ACC: 0.7235 -> 0.7321Helped
Add Contradiction Learning to Performance Learning (ablation)NCAT · EXAM, IRT, step 20ACC: 0.8417 -> 0.8516Helped
Set ν → 0 for deterministic selection instead of temperature-based stochastic selection (ablation)NCAT · ASSIST, IRT, step 20ACC: 0.7562 -> 0.7586Helped
Set ν → 0 for deterministic selection instead of temperature-based stochastic selection (ablation)NCAT · NIPS-EDU, IRT, step 20ACC: 0.7321 -> 0.7329Helped
Set ν → 0 for deterministic selection instead of temperature-based stochastic selection (ablation)NCAT · EXAM, IRT, step 20ACC: 0.8516 -> 0.8530Helped

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