CoChat
Sign Up
Cited by 55Intelligent Tutoring Systems and Adaptive LearningInnovations in Medical EducationRadiology practices and education

Adaptive tutorials versus web-based resources in radiology: A mixed methods comparison of efficacy and student engagement

Open access
Wong, Vincent, Smith, Ariella J., Hawkins, Nicholas J., Kumar, Rakesh K., Young, Noel, Kyaw, Merribel, Velan, Gary M.·2015·Academic Radiology
DOI 10.1016/j.acra.2015.07.002
Open this paper in CoChat — free account

Abstract

Rationale and objectives Diagnostic imaging is under-represented in medical curricula globally. Adaptive tutorials, online intelligent tutoring systems that provide a personalized learning experience, have the potential to bridge this gap. However, there is limited evidence of their effectiveness for learning about diagnostic imaging.Materials and methods We performed a randomized mixed methods crossover trial to determine the impact of adaptive tutorials on perceived engagement and understanding of the appropriate use and interpretation of common diagnostic imaging investigations. Although concurrently engaged in disparate blocks of study, 99 volunteer medical students (from years 1-4 of the 6-year program) were randomly allocated to one of two groups. In the first arm of the trial on chest X-rays, one group received access to an adaptive tutorial, whereas the other received links to an existing peer-reviewed Web resource. These two groups crossed over in the second arm of the trial, which focused on computed tomography scans of the head, chest, and abdomen. At the conclusion of each arm of the trial, both groups completed an examination-style assessment, comprising questions both related and unrelated to the topics covered by the relevant adaptive tutorial. Online questionnaires were used to evaluate student perceptions of both learning resources.Results In both arms of the trial, the group using adaptive tutorials obtained significantly higher assessment scores than controls. This was because of higher assessment scores by senior students in the adaptive tutorial group when answering questions related to topics covered in those tutorials. Furthermore, students indicated significantly better engagement with adaptive tutorials than the Web resource and rated the tutorials as a significantly more valuable tool for learning.Conclusions Medical students overwhelmingly accept adaptive tutorials for diagnostic imaging. The tutorials significantly improve the understanding of diagnostic imaging by senior students.

Integrity signals

Registry

Crossref · 10.1016/j.acra.2015.07.002

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Wong, V., Smith, A. J., Hawkins, N. J., Kumar, R. K., Young, N., Kyaw, M., & Velan, G. M.
Affiliations
  • UNSW Sydney (AU)
  • Westmead Hospital (AU)
Corresponding author
Velan, Gary M.
ORCID
Venue
Academic Radiology
Publisher
Elsevier BV
Published
2015-10
Volume
22
Issue
10
Pages
1299-1307
ISSN
1076-6332 (ISSN-L)
DOI
10.1016/j.acra.2015.07.002
OpenAlex
W1923702955
Cited by
55
References
21
Citations by year
2026: 2 · 2025: 4 · 2024: 15 · 2023: 2 · 2022: 9 · 2021: 3 · 2020: 4 · 2019: 7 · 2018: 4 · 2017: 2 · 2016: 1 · 2015: 1
FWCI
2.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Academic Radiology (publishedVersion)
  • PubMed (publishedVersion)
  • UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
  • UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
Funders
University of New South Wales
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
Keywords
intelligent tutoring systems, medical imaging education, medical students, student engagement, chest radiography, computed tomography, web-based learning resources, personalized learning, radiology curriculum
MeSH terms
Clinical Competence, Computer-Assisted Instruction, Curriculum, Humans, Surveys and Questionnaires, Radiology, Cross-Over Studies, Problem-Based Learning, Internet

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