Papers by Geoffrey T. LaFlair
Jump-Starting Item Parameters for Adaptive Language Tests (2021.emnlp-main)
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| Challenge: | Prior work has addressed ‘cold start’ estimation of item difficulties without piloting, but a multi-task generalized linear model with BERT features is needed to jump-start new items without pilot. |
| Approach: | They propose a multi-task generalized linear model with BERT features to jump-start new item difficulties without piloting them first. |
| Outcome: | The proposed model compares test-taker proficiency, item difficulty, and language proficiency frameworks like the Common European Framework of Reference (CEFR). |
Machine Learning–Driven Language Assessment (2020.tacl-1)
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| Challenge: | Language proficiency tests are cumbersome to create and maintain, and items may be copied and leaked or simply used too often. |
| Approach: | They propose a method that uses machine learning and natural language processing to induce proficiency scales and linguistic models to estimate item difficulty directly for computer-adaptive testing. |
| Outcome: | The proposed method produces scores that are reliable and reliable while generating item banks large enough to satisfy security requirements. |