Neel Sharma and Chaoyan Dong: Learning analytics—a potential tool in medical education

Technology utilization in medical training is ubiquitous. As instructors we recognise the fact that no lecture or tutorial is devoid of some form of technology. E-learning and mobile learning has introduced the potential for teaching to take place round the clock, at the convenience of the user. One such example is the highly talked about Massive Open Online Course (MOOC) initiative. However, it is still too early to ascertain the potential of such intervention from a medical perspective. Portfolio use is also technology based, as is the use of high fidelity simulation, although this is cost dependent, of course. Assessment is yet to follow suit globally but examples do exist of computer based testing, case in point the United States Medical Licensing Examination and the MRCP UK Specialty Certificate Examinations. 

A recent survey highlighting educational faculty attitudes on technology concluded that:

1. 9% strongly agree that online courses can achieve student learning outcomes equivalent to those of in person courses.
2. 51% felt that improving the educational experience required more active learning in the course.
3. 80% felt that it is very important for an online course to provide meaningful interaction between students and instructors (1).

Focusing on the latter point, we wonder how best this is achieved. Maybe medical schools globally should adopt learning analytics as an approach?

Learning analytics (LA) in a broad educational sense is not new. Yet its role in medical training has not been explored extensively. In brief, LA gathers data specific to the student cohort in question.

Information gained can:

1. Enhance the pedagogical support material, in particular for those students struggling to progress.
2. Produce more specific learning outcomes.
3. Act as evidence for assessment.

Of course, as educationalists, it is important to be comfortable with the reasons behind the occurrence of LA in the first instance. Evidence suggests that LA was utilized initially in a business slant in order to gain relevant data on consumer behavior and hence instigate advertising accordingly (2). It is well recognized that Google and Amazon utilize such methodology. From an educational standpoint utilizing such technology can initially seem troublesome; such big data can lead to issues with regards to value extraction, how such data may enhance the learning process, and how the data can be utilized to enhance institutional outcomes on an international scale (3). Literature suggests that a potential way forward is to focus on the basic science of learning with an understanding of how it takes place and therefore how best it can be supported (4). Even though big data analysis seems hard to digest initially, it is advised that working with a range of datasets is essential in order to truly optimize the learning environment (5). In addition, it is important to appreciate the ethical concerns data access may pose.

Instructors must determine a basic framework for such data use. And finally, whilst we all as educators can get carried away with technology with a potential institutional bias to succeed, the student must come first. Similar to any service industry; where the customer is always right, and in clinical medicine, where patients are more in tune with their physiology than any doctor they see. Therefore it is essential that learners are given a platform to provide feedback, and feedback that is acted upon.

LA is not without potential drawbacks, yet this is the norm for any new educational tool. This insight serves to highlight both sides of the story and we welcome readers’ thoughts in this regard.


1. Faculty Attitudes on Technology 2014 [cited 1 November 2014].

2. NMC Horizon Report 2013 [cited 1 November 2014].

3. Dawson S. “Seeing” the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology. 2010;41(5):736-52.

4. Ferguson R. Learning analytics: drivers, developments and challenges. Int J of Technology Enhanced Learning. 2012;4:304-17.

5. Mazza R, Dimitrova V. CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses. International Journal of Human-Computer Studies. 2007 2//;65(2):125-39.

Neel Sharma graduated from the University of Manchester and did his internal medicine training at The Royal London Hospital and Guy’s and St Thomas’ NHS Foundation Trust. Currently he is a gastroenterology trainee based in Singapore. 

Chaoyan Dong, PhD is a medical educationalist at the Centre for Medical Education, National University of Singapore.

Competing interests: None declared.