Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,040

The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.

Motivation to learn: an overview of contemporary theories

Open access
Cook, David A, Artino, Anthony R·2016·Medical Education

Abstract

Objective: To succinctly summarise five contemporary theories about motivation to learn, articulate key intersections and distinctions among these theories, and identify important considerations for future research. Results: Motivation has been defined as the process whereby goal‐directed activities are initiated and sustained. In expectancy‐value theory, motivation is a function of the expectation of success and perceived value. Attribution theory focuses on the causal attributions learners create to explain the results of an activity, and classifies these in terms of their locus, stability and controllability. Social‐ cognitive theory emphasises self‐efficacy as the primary driver of motivated action, and also identifies cues that influence future self‐efficacy and support self‐regulated learning. Goal orientation theory suggests that learners tend to engage in tasks with concerns about mastering the content (mastery goal, arising from a ‘growth’ mindset regarding intelligence and learning) or about doing better than others or avoiding failure (performance goals, arising from a ‘fixed’ mindset). Finally, self‐determination theory proposes that optimal performance results from actions motivated by intrinsic interests or by extrinsic values that have become integrated and internalised. Satisfying basic psychosocial needs of autonomy, competence and relatedness promotes such motivation. Looking across all five theories, we note recurrent themes of competence, value, attributions, and interactions between individuals and the learning context. Conclusions: To avoid conceptual confusion, and perhaps more importantly to maximise the theory‐building potential of their work, researchers must be careful (and precise) in how they define, operationalise and measure different motivational constructs. We suggest that motivation research continue to build theory and extend it to health professions domains, identify key outcomes and outcome measures, and test practical educational applications of the principles thus derived.

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 · 10.1111/medu.13074

Cites

17 of 17 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

Cited by

See all 1,040

Metadata

Authors
Cook, D. A., & Artino, A. R.
Venue
Medical Education
Publisher
Wiley
Published
2016-09-15
Volume
50
Issue
10
Pages
997-1014
ISSN
  • 0308-0110 (ISSN-L)
  • 1365-2923
OpenAlex
W2518885625
Cited by
1,040
References
17
FWCI
46.58
Type
journal article
Language
en
Open access
hybrid

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

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.