All parts of the item were important for predicting response time¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Unlike difficulty, response time prediction drew on all parts of the item rather than a single component. → Kang Xue 2020

Evidence¶

Kang Xue 2020¶

Kang Xue, Victoria Yaneva, Christopher Runyon, Peter Baldwin. (2020). Predicting the difficulty and response time of multiple choice questions using transfer learning. 15th Workshop on Innovative Use of NLP for Building Educational Applications. https://www.nwea.org/research/publication/predicting-the-difficulty-and-response-time-of-multiple-choice-questions-using-transfer-learning/

q2 · i? · associational · r2

In the same modeling study, the authors report that "all parts of the item were important for predicting the response time," contrasting with the stem-based pattern for difficulty; no effect size is printed in the available text.

"In addition, difficulty was best predicted using signal from the item stem (the description of theclinical case), while all parts of the item were important for predicting the response time."

Discussion¶

Related Claims¶

• Item difficulty was best predicted using signal from the item stem — related
• Transfer learning improves prediction of item difficulty when response time is used as an auxiliary task, but not the other way around — related
• Feature-based model predictive power derives from a large and varied set of features rather than a few dominant variables — a broader claim this one bears on