EOL judgments rely heavily on semantic relatedness, whose predictive validity appears to cap attainable EOL accuracy¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2017 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2020: tool and model accuracy changes quickly

Subclaims¶

q2 i? Across experiments, EOL judgments correlated almost perfectly with pre-standardized semantic relatedness (mean correlations .87–.91), and EOL accuracy tracked the cue's predictive validity for learnability. → Jemstedt 2017

Evidence¶

Jemstedt 2017¶

Jemstedt, A., Kubik, V., & Jönsson, F. U. (2017). What moderates the accuracy of ease of learning judgments? Metacognition and Learning, 12, 337–355. https://doi.org/10.1007/s11409-017-9172-3

q2 · i? · associational · r1

Secondary correlational analysis across the three experiments' high-variability conditions computed per-participant correlations of EOL judgments with pre-standardized semantic relatedness and of the cue with learnability. The authors state the cue's predictive validity "seems to limit how high the EOL accuracy can go".

"Interestingly, across all experiments and conditions the EOL judgments were almost perfectly correlated with the pre-standardized semantic relatedness. The mean correlations varied between .87 and .91."

Discussion¶

Related Claims¶

• EOL judgments are highly accurate when items vary widely in difficulty within a list, but poorly accurate when variation is small — related
• The type of EOL task (difficulty vs. trials-to-learn scale) does not reliably affect EOL accuracy — related