Big data in undergraduate medical education that consist the medical curriculum are beyond human abilities to be perceived and analyzed. The medical curriculum is the main tool used by teachers and directors to plan, design and deliver teaching activities, assessment methods and student evaluation in medical education in a continuous effort to improve it. It remains unexploited mainly for medical education improvement purposes. The emerging research field of Visual Analytics has the advantage to combine data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is lack of findings reporting use and benefits of Visual Analytics in medical education.
We analyzed data from the medical curriculum of an undergraduate medical program concerning teaching activities, assessment methods and results and learning outcomes in order to explore Visual Analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. We used Cytoscape to build networks of the identified aspects and visualize them.
The analysis and visualization of the identified aspects resulted in building an abstract model of the examined data from the curriculum presented in three different variants; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes and (iii) teaching methods, learning outcomes, examination results and gap analysis
This study identified aspects of medical curriculum. The implementation of VA revealed three novel ways of representing big data from undergraduate medical education. It seems to be a useful tool to explore such data and may have future implications on healthcare education. It also opens a new direction in medical informatics research.