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Learning Enhancement and Development

Session 3J - Paper 1

Small Data: Its importance in promoting learning engagement

Professor Clive Holtham - Cass Business School, City, University of London
Dr Martin Rich - Cass Business School, City, University of London

The experiences discussed involve active use of relatively neglected features of both synchronous and asynchronous technologies at City, University of London. The aim is to reach efficient assessments of student engagement and success via “small data”.

We examine two scenarios for use of small data: medium-size physical classes (50-200) and small-scale online classes (10-30), both in part relating to first year undergraduates. The former
uses small data from Moodle to track vulnerable first year students in their first term; the second involves the use of small data in synchronous educational conferencing (Adobe Connect).  Both scenarios could be applied across a range of courses to enhance student engagement and retention.  
The underlying engagement theory is taken from Beer, Clarke and Jones (2010) and Krause and Coates (2008).

We are interested in merging data obtainable from Moodle with backgrounds, grades, etc and we  focus on the smallest amount of data that can provide actionable information without front-line academics needing specialist expertise.

Nearly a decade ago, Dawson and McWilliam (2008) identified that VLEs often lack appropriate tools to extract and interpret the captured data. Moodle’s reports functionality remains modest but offers scope to extract small data that can provide profound insights that can enable personalised learning.

In the physical class we can use Moodle data to track students’ engagement with supporting material provided electronically and we can build in online activities to supplement the face-to-face contact.

For the webinars, we have shifted away from the facial video requirement; and are more interested in using video with a local document camera eg to share sketches.

In a webinar, there are many tools available which can be used very quickly to assess engagement.
This is a different type of small data.  One line text entries can be very valuable as they are fast, quick for all to read, and often show patterns immediately. Without body language, the voice becomes a primary guide to engagement.

Using these webinar features does, however, require both pre-planning and real-time technical proficiency in the webinar software.


Beer, Colin; Clarke, Ken and Jones, David (2010) Indicators of engagement Proceedings of ASCILITE Conference, 2010, Sydney

Dawson, S., & McWilliam, E. (2008). Investigating the application of IT generated data as an indicator of learning and teaching performance: Queensland University of Technology and the University of British Columbia.

Gašević, Dragan; Dawson, Shane,  Rogers, Tim and Gasevic, Danijela  (2016) Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success, The Internet and Higher Education, Volume 28, January 2016, Pages 68-84, ISSN 1096-7516, http://dx.doi.org/10.1016/j.iheduc.2015.10.002.


IE Business School (2016) WOW Room takes IE’s commitment to technology immersion in learning environments to the next level, News 20th October 2016  http://www.ie.edu/business-school/the-school/news-events/news/top-news/wow-room-takes-ie-commitment-to-technology-immersion-in-learning-environments-to-the-next-level/ [accessed 05/03/17]

Krause K and Coates H (2008) Students’ engagement in first year university.  Assessment and evaluation in higher education 33 (5) 493-505

Macfadyen L P and  Dawson S (2010) “Mining LMS Data to Develop an ‘Early Warning System’ for Educators: A Proof of Concept,” Computers & Education, 54 (2) 588–599