↓ Skip to main content

Structured feedback on students’ concept maps: the proverbial path to learning?

Overview of attention for article published in BMC Medical Education, May 2017
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
24 Mendeley
Title
Structured feedback on students’ concept maps: the proverbial path to learning?
Published in
BMC Medical Education, May 2017
DOI 10.1186/s12909-017-0930-3
Pubmed ID
Authors

Conran Joseph, David Conradsson, Lena Nilsson Wikmar, Michael Rowe

Abstract

Good conceptual knowledge is an essential requirement for health professions students, in that they are required to apply concepts learned in the classroom to a variety of different contexts. However, the use of traditional methods of assessment limits the educator's ability to correct students' conceptual knowledge prior to altering the educational context. Concept mapping (CM) is an educational tool for evaluating conceptual knowledge, but little is known about its use in facilitating the development of richer knowledge frameworks. In addition, structured feedback has the potential to develop good conceptual knowledge. The purpose of this study was to use Kinchin's criteria to assess the impact of structured feedback on the graphical complexity of CM's by observing the development of richer knowledge frameworks. Fifty-eight physiotherapy students created CM's targeting the integration of two knowledge domains within a case-based teaching paradigm. Each student received one round of structured feedback that addressed correction, reinforcement, forensic diagnosis, benchmarking, and longitudinal development on their CM's prior to the final submission. The concept maps were categorized according to Kinchin's criteria as either Spoke, Chain or Net representations, and then evaluated against defined traits of meaningful learning. The inter-rater reliability of categorizing CM's was good. Pre-feedback CM's were predominantly Chain structures (57%), with Net structures appearing least often. There was a significant reduction of the basic Spoke- structured CMs (P = 0.002) and a significant increase of Net-structured maps (P < 0.001) at the final evaluation (post-feedback). Changes in structural complexity of CMs appeared to be indicative of broader knowledge frameworks as assessed against the meaningful learning traits. Feedback on CM's seemed to have contributed towards improving conceptual knowledge and correcting naive conceptions of related knowledge. Educators in medical education could therefore consider using CM's to target individual student development.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 23 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 21%
Student > Ph. D. Student 4 17%
Unspecified 3 13%
Professor > Associate Professor 3 13%
Student > Bachelor 3 13%
Other 6 25%
Readers by discipline Count As %
Medicine and Dentistry 7 29%
Social Sciences 6 25%
Unspecified 4 17%
Arts and Humanities 2 8%
Decision Sciences 1 4%
Other 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 October 2017.
All research outputs
#3,188,254
of 12,072,146 outputs
Outputs from BMC Medical Education
#598
of 1,625 outputs
Outputs of similar age
#85,363
of 270,039 outputs
Outputs of similar age from BMC Medical Education
#14
of 45 outputs
Altmetric has tracked 12,072,146 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,625 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,039 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.