Background: The use of virtual patients (VPs), due to their high complexity and/or inappropriate sequencing with other instructional methods, might cause a high cognitive load, which hampers learning. Aim: To investigate the efficiency of instructional methods that involved three different applications of VPs combined with lectures. Method: From two consecutive batches, 171 out of 183 students have participated in lecture and VPs sessions. One group received a lecture session followed by a collaborative VPs learning activity (collaborative deductive). The other two groups received a lecture session and an independent VP learning activity, which either followed the lecture session (independent deductive) or preceded it (independent inductive). All groups were administrated written knowledge acquisition and retention tests as well as transfer tests using two new VPs. All participants completed a cognitive load questionnaire, which measured intrinsic, extraneous and germane load. Mixed effect analysis of cognitive load and efficiency using the R statistical program was performed. Results: The highest intrinsic and extraneous load was found in the independent inductive group, while the lowest intrinsic and extraneous load was seen in the collaborative deductive group. Furthermore, comparisons showed a significantly higher efficiency, that is, higher performance in combination with lower cognitive load, for the collaborative deductive group than for the other two groups. Conclusion: Collaborative use of VPs after a lecture is the most efficient instructional method, of those tested, as it leads to better learning and transfer combined with lower cognitive load, when compared with independent use of VPs, either before or after the lecture.

Background: The use of virtual patients (VPs), due to their high complexity and/or inappropriate sequencing with other instructional methods, might cause a high cognitive load, which hampers learning. Aim: To investigate the efficiency of instructional methods that involved three different applications of VPs combined with lectures. Method: From two consecutive batches, 171 out of 183 students have participated in lecture and VPs sessions. One group received a lecture session followed by a collaborative VPs learning activity (collaborative deductive). The other two groups received a lecture session and an independent VP learning activity, which either followed the lecture session (independent deductive) or preceded it (independent inductive). All groups were administrated written knowledge acquisition and retention tests as well as transfer tests using two new VPs. All participants completed a cognitive load questionnaire, which measured intrinsic, extraneous and germane load. Mixed effect analysis of cognitive load and efficiency using the R statistical program was performed. Results: The highest intrinsic and extraneous load was found in the independent inductive group, while the lowest intrinsic and extraneous load was seen in the collaborative deductive group. Furthermore, comparisons showed a significantly higher efficiency, that is, higher performance in combination with lower cognitive load, for the collaborative deductive group than for the other two groups. Conclusion: Collaborative use of VPs after a lecture is the most efficient instructional method, of those tested, as it leads to better learning and transfer combined with lower cognitive load, when compared with independent use of VPs, either before or after the lecture.