MD Connect research
An emerging branch of data mining, learning analytics involves the capture, analysis, reporting and application of data related to learning and teaching. With the widespread adoption of enterprise level learning management and delivery tools, learning analytics techniques are increasingly being adopted in an effort to capitalise on the mass of incidental data generated and captured by these systems. Most commercial learning systems now either support the use of, or include, their own embedded learning analytics tools with the expectation that the analytics data generated by these tools can be used to report on, inform and potentially improve on learning and teaching requirements, practices and performance across a range of contexts, from the institutional right down to the individual learner.
MD Connect is relatively unique among learning systems in that, in addition to comprehensive user management and reporting, it features a high level of horizontal (within year) and vertical (between years) curriculum integration. Because of this, it provides a strong and meaningful context for capturing students' online study and learning behaviours and for analysing and interpreting these behaviours in relation to both specific learning activities and the broader (MD) curriculum it supports.
By drawing on the rich usage data generated by the MD Connect system and the development and application of suitable learning analytics tools and techniques our aim is to develop a richer and more nuanced understanding of how and to what degree medical students' learning behaviours vary from student to student, over time, and in response to a diversity of learning needs. We will undertake a program of research around medical students adoption and use of learning technologies within the Melbourne MD curriculum. This research will be based on the development and application of learning analytics to describe, analyse and interpret learners' actions, behaviours and performance. The research program will cover a wide range of students' learning activities and behaviours within or mediated by the MD Connect platform although our initial focus will be on using learning analytics to support existing areas of research. These include students’ selection and use of learning resources, the implementation and uptake of mobile technologies, the development of students’ clinical portfolios and the provision of individualised and contextualised feedback on assessment and progress.
Potential advantages of this research include:
To MD students
- Improved discovery of and access to learning resources
- Improved management of learning activities and study practices
- Improved reporting of performance and progression
- Provision of individualised activity-specific feedback
To MD staff and educators
- Improved metrics and reporting of student activity
- Improved metrics and reporting of student performance and progression
- Improved metrics and reporting of learning activity performance
- Students' selection and use of digital resources to support their learning. This includes the use of explicitly recommended (i.e. timetabled), generally recommended and discovered resources. In particular we will explore variations between users and in response to different learning activities and objectives and the link (if any) between use and performance and progression. This theme is designed to improve our ability to deliver high quality, targeted resources, improve students' ability to match resources to learning activities and objectives and to better support natural variations in students learning styles.
- Variations in students' study patterns. Situational and temporal variations in use of the MD Connect platform will be analysed to assess the changes in students' learning requirements at different stages of the MD curriculum. Findings from this research should enhance our ability to better and more flexibly support students' learning needs within a demanding and diverse curriculum, including potentially problematic transitions from bioscience to clinical learning, campus to hospital based learning, and formal (lecture-based) to informal learning.
- Variations in students' learning paths. The MD Connect platform is intended to support a range of learner styles and these are often represented within the navigational choices and activity and resource selections students make while using the platform. This research theme is complementary to themes 1 and 2 and will improve our ability to assess the effectiveness of key learning styles against different learning activities and contexts.
- students' development and use of clinical portfolios. The creation of patient records (de-identified records of patient interviews) and procedure logs (de-identified records of clinical procedures) are core learning activities for clinically based students. Analysis of students' use and management of their clinical portfolios within this research theme has the potential to produce useful metrics of progress and mastery to supplement those derived through existing assessment tasks and procedures.
- Personalised feedback on progress. Analysis of students' activity within the MD Connect platform, including access to learning activities and resources, combined with results from key assessment tasks, can be used to develop novel measures of progress. These measures, which can include a combination of individual and cohort/group results, can be used to develop progress reports and/or activity-based feedback that can be personalized and targeted to a variety of MD Connect users. Work within this theme will include the development and implementation of learner-centric analytics 'dashboards'.
Publications and presentations
Judd T, Elliott K. Use of lecture resources by first year medical students. Australasian J Educ Tech. Forthcoming 2016.
Judd T, Elliott K. Sharing of learning resources by medical students. Brit J Educ Technol. Forthcoming 2016.
Dr Terry Judd - firstname.lastname@example.org (lead)
Dr Anna Ryan - email@example.com (assessment and feedback)
Dr Lisa Cheshire - firstname.lastname@example.org (clinical portfolios)
Professor Geoff McColl - email@example.com (head of school)
Professor Steve Trumble - firstname.lastname@example.org (head of department)
If you would like further information on the MD Connect program of research in general or any of the specific research themes then please contact the research team at email@example.com. DME researchers who are interested in accessing or utilising analytics data from MD Connect in their work and other researchers from within or outside The University of Melbourne who are interested in developing research collaborations should contact the program lead.