Model progression benefits learning only in sufficiently complex simulations; in a simple domain it was less efficient than full complexity from the start¶
Claim · All claims
Evidence · 1 study (3 entries) · reviewr3·q2
Currency · newest study 1998 · not yet checked against its sources · 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? In a simple disease-spread simulation, model progression had no overall positive effect on performance and was less efficient than immediate full complexity. → Ton de Jong 1998
q2 i? In a harmonic oscillation simulation, model progression enlarged students' intuitive knowledge (but not conceptual knowledge) relative to an environment without progression. → Ton de Jong 1998 (2)
q2 i? With increasing control over variables in a Newtonian motion simulation, learners scored significantly higher on application of rules than learners with full control from the start. → Ton de Jong 1998 (3)
Evidence¶
Ton de Jong 1998¶
Ton de Jong, Wouter R. van Joolingen. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Review of Educational Research. https://telearn.hal.science/hal-00190680v1
q2 · i? · review · r3
The review reports Quinn and Alessi's experiment with a disease-spread simulation with four input variables, comparing groups given full access immediately against staged access; "model progression had no overall positive effect on performance" in this simple, non-interacting domain.
"Their data revealed that Scientific Discovery Learning with Computer Simulations 27 model progression had no overall positive effect on performance."
Ton de Jong 1998 (2)¶
Ton de Jong, Wouter R. van Joolingen. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Review of Educational Research. https://telearn.hal.science/hal-00190680v1
q2 · i? · review · r3
The review reports Swaak et al.'s SETCOM study on harmonic oscillation, where the model developed from free through damped to externally forced oscillation; model progression enlarged "intuitive knowledge" but not conceptual knowledge.
"Swaak et al. (1996) found that model progression was successful in enlarging the students’ intuitive knowledge (but not their conceptual knowledge) as compared to an environment without model progression."
Ton de Jong 1998 (3)¶
Ton de Jong, Wouter R. van Joolingen. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Review of Educational Research. https://telearn.hal.science/hal-00190680v1
q2 · i? · review · r3
The review reports Rieber and Parmley's Newtonian motion study; learners given gradually increasing control "scored significantly higher at a test measuring application of rules" than full-complexity learners; no effect size is printed.
"Rieber and Parmley (1995) found, in the area of Newtonian motion, that subjects learning with a simulation that presented an increasing control over variables, scored significantly higher at a test measuring application of rules, than subjects who could exercise control in its full complexity from the start."
Discussion¶
Related Claims¶
- Multiple-hypothesis strategies improve performance only when simulation complexity is low — related
- Segmentation Benefits Shrink With Expertise — related
- Simulation-based learning has a large positive overall effect on complex skills in higher education (g = 0.85) — related
- Teams receiving part-task congruent representational tools (causal then simulation) outperformed teams with partly matched or mismatched tools on a complex business-economics learning task — related
- KLI complexity-matching hypothesis: instructional principles of a given complexity are effective for knowledge components of equal or greater complexity but not less complex ones — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
model-progression-complexity-boundary |
title |
Model progression benefits learning only in sufficiently complex simulations; in a simple domain it was less efficient than full complexity from the start |
description |
Model progression benefits learning only in sufficiently complex simulations; in a simple domain it was less efficient than full complexity from the start |
status |
review |
evidence_strength |
mixed |
newest_evidence |
1998 |
generated |
by process:wiki-ingest on 2026-10-16 |
trust |
unverified — no one has confirmed this page's content |
sources |
3 — ton-de-jong-1998, ton-de-jong-1998-2, ton-de-jong-1998-3 |
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