A tutoring system can adjust its behaviour in response to a dynamically modelled motivational state, though some motivational rules are not appreciated by learners¶
Claim · All claims
Evidence · 1 study · designr2·q2
Currency · newest study 2016 · checked against its sources 2026-10-28 (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? In a brief formative evaluation, MORE adjusted its behaviour as intended, but some tactics, such as refusing requested help when confidence was adequate but independence low, were not appreciated by participants. → du Boulay 2016
Evidence¶
du Boulay 2016¶
du Boulay, B., & del Soldato, T. (2016). Implementation of Motivational Tactics in Tutoring Systems: 20 years on. International Journal of Artificial Intelligence in Education, 26, 170-182. https://doi.org/10.1007/s40593-015-0052-1
q2 · i? · design · r2
The authors' brief formative evaluation of MORE gathered comments from a small number of participants about their experiences. It showed the system "did indeed adjust its behavior", but the help-refusal tactic raised issues about the social rules of felicitous engagement between learner and system.
"This showed that the system did indeed adjust its behavior, but that some of the rules were not appreciated: such as when the system refused to give help when requested if it thought that the learner did not need help"
Discussion¶
Related Claims¶
- MORE's effort-based motivational strategy resembles the later Wayang Outpost strategy in effort feedback and difficulty adjustment — related
- Adaptive learning functions as the algorithmic engine that operationalizes personalized learning — a broader claim this one bears on
- Laboratory-identified behavioural markers of states such as confusion can, with care, indicate the same state in real online learning environments — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
more-system-adjusted-behavior-to-motivational-state |
title |
A tutoring system can adjust its behaviour in response to a dynamically modelled motivational state, though some motivational rules are not appreciated by learners |
description |
A tutoring system can adjust its behaviour in response to a dynamically modelled motivational state, though some motivational rules are not appreciated by learners |
status |
review |
evidence_strength |
weak |
newest_evidence |
2016 |
source_checked |
2026-10-28 |
generated |
by process:wiki-ingest on 2026-10-28 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — du-boulay-2016 |
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