Training learners with an effort-and-performance algorithm via video modeling improved monitoring accuracy, task selection accuracy, and learning outcomes¶
Claim · All claims
Evidence · 1 study · reviewr2·q2
Currency · newest study 2020 · checked against its sources 2026-10-22 (automated, 1 of 1 entry) · not reviewed by a person
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.
q2 i? An algorithm combining performance and mental-effort appraisals to choose easier, equally difficult, or more difficult tasks, conveyed by video modeling, improved monitoring accuracy, task selection accuracy, and learning outcomes. → Tina Seufert 2020
Evidence¶
Tina Seufert 2020¶
Tina Seufert. (2020). Building Bridges Between Self-Regulation and Cognitive Load—an Invitation for a Broad and Differentiated Attempt. Educational Psychology Review, 32, 1151–1162. https://doi.org/10.1007/s10648-020-09574-6
q2 · i? · review · r2
The review reports, citing van Gog, Hoogerheide, and van Harsel, a series of studies training learners to combine actual performance and mental-effort appraisals for task choice; the video-modeled algorithm "improved learners monitoring accuracy and the task selection accuracy and above all also improved learning outcomes."
"This algorithm was conveyed by a video modeling example, and it actually improved learners monitoring accuracy and the task selection accuracy and above all also improved learning outcomes."
Discussion¶
Related Claims¶
- Video modeling training of self-assessment and task-selection skills improves secondary students' problem-solving performance after a self-regulated learning phase — a broader claim this one bears on
- Task-selection skills trained with video modeling examples in biology transfer to task selection in a different domain (math) — related
- Heuristic task-selection training is not more effective for transfer than algorithmic training — related
- Segmenting complex dynamic visualizations into pieces with pauses improves learning outcomes, especially transfer performance, at least for novices, compared with non-segmented presentations — related
- Effort appraisals correlate negatively with monitoring judgments: high mental effort is associated with poorer performance judgments — related
- Training students to select tasks using a combination of self-assessed performance and invested mental effort improves self-assessment, task selection, and posttest performance, but one follow-up study found no transfer to a new problem domain — possibly the same claim (merge candidate)
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
effort-performance-algorithm-training-improves-task-selection |
title |
Training learners with an effort-and-performance algorithm via video modeling improved monitoring accuracy, task selection accuracy, and learning outcomes |
description |
Training learners with an effort-and-performance algorithm via video modeling improved monitoring accuracy, task selection accuracy, and learning outcomes |
status |
review |
evidence_strength |
weak |
newest_evidence |
2020 |
source_checked |
2026-10-22 |
generated |
by process:wiki-ingest on 2026-10-19 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — tina-seufert-2020 |
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