Test-enhanced learning improves learner attendance during a laparoscopic box trainer simulation program

Aust N Z J Obstet Gynaecol. 2022 Aug;62(4):589-595. doi: 10.1111/ajo.13528. Epub 2022 Apr 28.

Abstract

Background: Laparoscopy is the gold standard approach for many surgical procedures, but it is a complex skill to learn. Laparoscopic simulation training may help, but it is unclear how to best engage trainees in these programs. Test-enhanced learning (TEL) uses regular, well-defined assessments of performance throughout the training phase of learning.

Aim: The aim of this study was to assess the effects of TEL on a laparoscopic simulation program involving a cohort of medical student volunteers.

Materials and methods: A prospective cohort study was performed with a convenience sample of 40 medical students. Students were recruited to participate in a ten-week laparoscopic simulation program. Twenty students participated in a laparoscopic surgical program with TEL ('TEL group'), and 20 students participated in a standard laparoscopic simulation program ('control group').

Results: Attendance in the TEL group was significantly higher than in the standard group (71 vs 51.5%, P = 0.03). There was no difference between groups in mean time scores. Four themes were identified in qualitative data drawn from student surveys - personal traits and motivators, training context, clear goals and feedback enabling understanding of one's own performance.

Conclusion: Testing laparoscopic skills throughout a learning program, in conjunction with individualised feedback and tracking of learning trajectory, increases trainee attendance. Laparoscopic simulation training programs are encouraged to reflect on the pedagogic framework in which their procedural skills training operates.

Keywords: laparoscopic simulation; laparoscopic surgery; surgical simulation; test-enhanced learning.

MeSH terms

  • Clinical Competence
  • Computer Simulation
  • Humans
  • Laparoscopy* / education
  • Prospective Studies
  • Simulation Training*

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