gms | German Medical Science

Introduction: Medical education and training faces many challenges such as rapidly growing amount of knowledge and therefore high complexity of the subjects [1] or seasonal diseases and an increasing amount of critically ill patients in hospitals [2]. Virtual patients have been used in medical education and training for many years and have shown to be a solution to these problems [2]. In addition, studies have shown an enhancement in learning results while using virtual patients [3]. For further improvement and optimal usage of virtual patients the combination with learning analytics is pursued. Learning analytics aims to evaluate, analyse and interpret user data that is created in the learning process and provide the participants, teachers and learners, with new insights on how to improve teaching and better understand education [4].

Methods: Although learning analytics have been available for some time, the usage in virtual patients has not been as high as anticipated. Therefore, workshops with the teaching personnel were held to evaluate their needs. Based on the ideas of the workshop the learning data was formatted and made available for the authors via the authoring tool of the virtual patient software CAMPUS CASUS. The authors were provided with different possibilities of viewing and exporting the data from plain answers to prepared graphics and tables. The data source can be limited from all available anonymized data in a course down to individual data. Afterwards it was made possible to choose time slots and to build cohorts of specific students to use the data in certain blended learning scenarios. Workshops and trainings on how to use the data properly were provided several times. Feedback on the usage of these data reports was given by teachers and students regularly over time via different channels like interviews, support requests and evaluations.

Results: Based on the number of retrievals a higher usage of the learning analytics functions and evaluations can be reported. It was also stated that the usage of the learning analytics data led to a more enhanced learning experience on both sides. Teachers could identify misconceptions of their own on which topics they sensed as simple and the learners didn’t and the other way around. Learners could identify for themselves where they had individually additional backlog.

Discussion: These results show that the usage of learning analytics data can enhance the learning experience with virtual patients and give the participants a deeper understanding of the learning process. Furthermore, it was also learned that providing the data is not sufficient to reach its full potential. Teachers need proper instructions in workshops and additional support during the application of the data in their courses. Especially in scenarios with different teaching personnel and tutors the data must be provided via low-threshold access and with back-office support. In prospect the objective is a study to collect qualitative data on learning improvements.

Conclusion: In conclusion learning analytics can enhance the learning experience and results of virtual patients in medical education and training.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.