Adding MicroDYN rule application significantly improves prediction of overall problem solving, planning, tool usage, and evaluating progress, but not analyzing givens¶
Claim · All claims
Evidence · 1 study · associationalr2·q3
Currency · newest study 2016 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2019: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q3 i? Adding rule application significantly improved prediction of overall problem solving (R2change = 0.14), planning (0.09), tool usage (0.11), and evaluating progress (0.11), all p < 0.05, but not analyzing givens (R2change = 0.05, p = 0.12). → Shute 2016
Evidence¶
Shute 2016¶
Shute, V. J., Wang, L., Greiff, S., Zhao, W., & Moore, G. (2016). Measuring problem solving skills via stealth assessment in an engaging video game. Computers in Human Behavior, 63, 106-117. https://doi.org/10.1016/j.chb.2016.05.047
q3 · i? · associational · r2
Hierarchical regression analyses from the validation study predicting the overall estimate and each facet from MicroDYN components. The article reports significant R2change values for overall, planning, tool usage, and evaluating progress, and a non-significant result for analyzing givens; adding rule acquisition first contributed nothing (all p > 0.05).
"adding rule application significantly improved the model predicting the overall problem solving estimate ( R2 change ¼ 0.14, p < 0.05), planning ( R2 change ¼ 0.09, p < 0.05), tool usage ( R2 change ¼ 0.1 1,p < 0.05), and evaluating progress ( R2 change ¼ 0.1 1,p < 0.05). However, the same model did not significantly predict analyzing givens ( R2 change ¼ 0.05, p ¼ 0.12)."
Discussion¶
Related Claims¶
- Facet difficulty and intercorrelation patterns: analyzing givens was easiest, evaluating progress hardest, and planning correlated 0.98 with the overall estimate — related
- Stealth assessment facets show good internal consistency (Cronbach's alpha = 0.76) while MicroDYN reliabilities were low in this middle-school sample — related
- MicroDYN is a stronger predictor of the in-game problem solving estimates than Raven's, with the rule-application facet more relevant than rule acquisition — related
- Raven's and MicroDYN jointly explain 22% of variance in the stealth assessment estimate, with MicroDYN adding 5.7% unique variance beyond Raven's — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
rule-application-predicts-facets-except-analyzing-givens |
title |
Adding MicroDYN rule application significantly improves prediction of overall problem solving, planning, tool usage, and evaluating progress, but not analyzing givens |
description |
Adding MicroDYN rule application significantly improves prediction of overall problem solving, planning, tool usage, and evaluating progress, but not analyzing givens |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2016 |
recheck_by |
2019 |
generated |
by process:wiki-ingest on 2026-10-16 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — shute-2016 |
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