Teaching Award leads Dr David Johnson to Continuing Education

Teaching Award leads Dr David Johnson to Continuing Education

The Centre's Senior Research Associate Dr David Johnson recently won a Maths, Physics and Life Sciences Division Teaching Award for his project entitled 'A "Quantified Self" Approach to Teaching Data Science'.

Now his innovative approach to teaching Data Science, using Fitbit wristbands, has been adopted by Oxford University's Continuing Education Department in their 10 week Applied Data Science evening course, starting April 2017.

The course will provide students with a broad knowledge of data science as well as hands-on experience of some of the most advanced tools and technologies in the field.

Students will be given the opportunity to contribute towards a class-aggregated dataset in a 'Quantified Self' approach to teaching by using Fitbits, personal wearable devices that collect activity data. Dr Johnson will also teach part of the course, focusing on practical aspects of using Big Data technologies such as data engineering and using cloud computing infrastructure.

Dr Johnson, who was recently appointed as a Junior Research Fellow at Kellogg College Oxford, says "Privacy, ethics and the social implications of using data are now often considered, but when using open data for teaching, it is easy to forget these aspects since the data is typically pre-processed and anonymised and summarised to make it openly usable. The Quantified Self-approach to teaching brings these aspects to the forefront".

He adds, "My hope is that a Quantified Self approach to teaching will enthuse students far more than utilising an existing data set that is openly available, by empowering individual students to contribute their own data to the classroom environment, and enabling a sense of collective comradery when considering such data collected and explored in aggregate. What would be more exciting for a student than performing a data science exercise on themselves?".

Data science is a discipline that deals with collecting, preparing, managing, analysing, interpreting and visualising large and complex datasets. Researchers working with 'Big Data' can be found working in diverse scientific areas such as astrophysics, particle physics, biology, meteorology, medicine, finance, healthcare and social sciences.

An increasing number of people are equipped with connected devices and sensors, providing more efficient and convenient ways to collect personal activity information such as tracking physical activity, food intake and sleep quality, as well as health monitoring of factors such as oxygen saturation, blood glucose level and heart rate. The scale and richness of sensor data being collected and analysed is rapidly growing, and we are currently observing the rise of people-centric systems to considerably extend current human capabilities in acquiring and monitoring personal activity information. This movement is often termed the 'Quantified Self', whereby individuals can gather this kind of data to self-monitor and adapt their behaviour accordingly.

By participating in an exercise on personal data, it is hoped that students will gain an all-round appreciation for all aspects of data-driven research – from issues around data collection (where personal data issues are increasingly important today), data fusion, cleansing, processing, through to visualization. Given that students will 'bring their own' data, an added benefit that should enhance the learning experience will be in empowering the student through a sense of individual ownership of the tutorial exercises (since the exercise is applied to their own data) as well as collective ownership as a class (for exercises on the data in aggregate). Giving students the chance to explore guaranteed new data should also enthuse them further, since everybody involved will truly be exploring the unknown, rather than being guided by a pre-existing dataset that might have been used many times over.

Dr Johnson also aims to incorporate the Fitbit method in a trial as part of the Department of Computer Science's part-time MSc Software Engineering Programme, on which he is a Guest Lecturer and Teaching Assistant for a module on "Cloud Computing & Big Data".

Following the technology trial, Dr Johnson will write a report or paper on its integration with teaching methods, and an evaluation of its success. If the trial project proves successful in teaching quantitative methods in such an innovative way, the method could be rolled out across other subjects throughout the MPLS division, such as in mathematics, statistics, and biomedical sciences.

More information

The MPLS (Mathematical, Physical and Life Sciences) Divisional teaching awards are given out in June/July each year to celebrate success, and recognise and reward excellence in teaching. Awards are available to all those who teach, including graduate students, postdoctoral researchers and learning support staff. Dr Johnson was formally given the award at the MPLS Summer Reception on 12th July.