Significant learner characteristics explained 17% and 14% of variance in prediction bias and 11% and 12% in postdiction bias, so characteristics were slightly more relevant for predictions¶

Claim · All claims
Evidence · 1 study · associational r2 · q3
Currency · newest study 2022 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? The statistically significant learner characteristics explained a moderate amount of variance in prediction bias (17% and 14%) and a small amount in postdiction bias (11% and 12%). → Stefanie Golke 2022

Evidence¶

Stefanie Golke 2022¶

Stefanie Golke, Tim Steininger, & Jörg Wittwer. (2022). What Makes Learners Overestimate Their Text Comprehension? The Impact of Learner Characteristics on Judgment Bias. Educational Psychology Review. https://doi.org/10.1007/s10648-022-09687-0

q3 · i? · associational · r2

Across the four hierarchical regression analyses (prediction/postdiction bias for factual/inference questions) in the N = 255 sample, the article reports the significant characteristics "explained a moderate amount (cf. Cohen, 1988) of 17% and 14% of variance in the measures of prediction bias."

"the statistically significant learner characteristics explained a moderate amount (cf. Cohen, 1988) of 17% and 14% of variance in the measures of prediction bias and a small amount of 11% and 12% of variance in the postdiction bias measures"

Discussion¶

Related Claims¶

• For postdiction bias on factual questions, underestimation was prevalent for females, higher reading skill, and lower self-perceived topic knowledge and metacomprehension ability — related
• For inference questions, lower reading skill, higher self-efficacy, and higher self-reported monitoring strategies were associated with stronger overestimation, with openness associated only for predictions — related