Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 301

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Adaptive Motivation and Emotion in Education: Research and Principles for Instructional Design

Linnenbrink-Garcia, Lisa, Patall, Erika A., Pekrun, Reinhard·2016·Policy Insights from the Behavioral and Brain Sciences

Abstract

Students frequently experience various types of motivation and emotion that contribute to their engagement and learning. However, translating research on motivation and emotion into educational practice and policy has so far been limited. To facilitate greater synergy among research, practice, and policy, this overview addresses educationally relevant motivation and emotion. This summary discusses different forms of motivation or emotion, their relevant theoretical basis, evidence on how they relate to academic engagement and learning, and potential classroom supports for adaptive motivation and emotion. The article concludes with five instructional design principles that can guide educators and policymakers in promoting adaptive student motivation and emotion: (a) support students’ feelings of competence, (b) enhance autonomy, (c) use personally relevant and active tasks, (d) emphasize learning and de-emphasize social comparison, and (e) encourage feelings of belonging.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/2372732216644450

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 18 references

Cited by

See all 301

Metadata

Authors
Linnenbrink-Garcia, L., Patall, E. A., & Pekrun, R.
Venue
Policy Insights from the Behavioral and Brain Sciences
Published
2016-07-08
Volume
3
Issue
2
Pages
228-236
ISSN
  • 2372-7322 (ISSN-L)
  • 2372-7330
OpenAlex
W2509003089
Cited by
301
References
18
FWCI
13.88
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Adaptive Motivation and Emotion in Education: Research and Principles for Instructional Design. https://app.cochat.ai/papers/10.1177/2372732216644450
The paper
Linnenbrink-Garcia, L., Patall, E. A., & Pekrun, R. (2016). Adaptive Motivation and Emotion in Education: Research and Principles for Instructional Design. Policy Insights from the Behavioral and Brain Sciences. https://doi.org/10.1177/2372732216644450

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.