Hurst exponents predict daily training self-explanation quality over and above pretest self-explanation ability¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2016 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Hurst exponents were a significant predictor of daily self-explanation scores over and above pretest scores, accounting for 18% additional variance (R² = .44, F(1,37) = 11.93, p < .01). → Erica L. Snow 2016

Evidence¶

Erica L. Snow 2016¶

Erica L. Snow, Aaron D. Likens, Laura K. Allen, & Danielle S. McNamara. (2016). Taking Control: Stealth Assessment of Deterministic Behaviors Within a Game-Based System. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-015-0085-5

q2 · i? · associational · r2

Linear regression with 40 students: model one showed pretest self-explanation predicted daily training scores (R² = .26, F(1,38) = 13.60, p < .01); model two added Hurst exponents, which the article reports "accounted for 18 % of the additional variance" (R² = .44, F(1,37) = 11.93, p < .01). No standardized effect size is printed.

"Hurst exponents were a significant predictor of daily self-explanation scores over and above the pretest self-explanation score (R2 =. 4 4 ,F(1,37) = 11.93, p <. 0 1"

Discussion¶

Related Claims¶

• Hurst exponents do not significantly predict retention self-explanation quality over and above pretest scores — related
• Deterministic interaction patterns relate to delayed retention self-explanation quality but not posttest performance — related
• Students who interact more deterministically within iSTART-ME generate higher quality self-explanations during training — related
• Hurst exponents are unrelated to the frequency of interacting with any specific game-based feature — related
• Surrogate analysis validates that observed Hurst exponents reflect genuine persistence rather than short-series artifacts — related