Audience response system: effect on learning in family medicine residents

Fam Med. Jul-Aug 2004;36(7):496-504.

Abstract

Background and objectives: The use of an electronic audience response system (ARS) that promotes active participation during lectures has been shown to improve retention rates of factual information in nonmedical settings. This study (1) tested the hypothesis that the use of an ARS during didactic lectures can improve learning outcomes by family medicine residents and (2) identified factors influencing ARS-assisted learning outcomes in family medicine residents.

Methods: We conducted a prospective controlled crossover study of 24 family medicine residents, comparing quiz scores after didactic lectures delivered either as ordinary didactic lectures that contained no interactive component, lectures with an interactive component (asking questions to participants), or lectures with ARS.

Results: Post-lecture quiz scores (maximum score 7) were 4.25 +/- 0.28 (61% correct) with non-interactive lectures, 6.50 +/- 0.13 (n=22, 93% correct) following interactive lectures without ARS, and 6.70 +/- 0.13 (n=23, 96% correct) following ARS lectures. The difference in scores following ARS or interactive lectures versus non-interactive lectures was significant (P <.001). Mean quiz scores declined over 1 month in all three of the lecture groups but remained highest in the ARS group. Neither lecture factors (monthly sequence number) nor resident factors (crossover group, postgraduate training year, In-Training Examination score, or post-call status) contributed to these differences, although postcall residents performed worse in all lecture groups.

Conclusions: Both audience interaction and ARS equipment were associated with improved learning outcomes following lectures to family medicine residents.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cross-Over Studies
  • Family Practice / education*
  • Humans
  • Internship and Residency*
  • Knowledge of Results, Psychological*
  • Linear Models
  • Prospective Studies