Online collaborative learning: Quantifying how people learn together online

Abstract

Background: Curtis and Lawson described a simple but useful method to quantify the interactions of students collaboratively learning online using content analysis to categorise behaviour. We extended Curtis and Lawson's method by allowing responses to interactions to be recorded and analysed. Populations can be partitioned into groups of arbitrary size to address a variety of research questions.

Aims: This paper describes an attempt to explore the utility of this extended method when applied to real online collaborative learning.

Method: Using an illustrative example from a postgraduate online medical course for general practitioners, we show how rich the dynamics of online interaction can be and how courses can be analysed to suggest improvements.

Results: We found that tutors and students differ in how they behave when learning online and according to task type.

Conclusions: We propose that our method could be useful to measure the effectiveness of collaborative exercises and can be applied wherever research is committed into online group behaviour.

Additional information

Notes on contributors

Alan James Salmoni

ALAN JAMES SALMONI is a postdoctoral fellow at the Department of Dermatology, Cardiff University whose work focuses on human-computer interaction.

Maria L. Gonzalez

DR MARIA L GONZALEZ is a senior clinical lecturer at the Department of Dermatology, Cardiff University, where she leads the teaching and learning activities of the department in addition to participating in clinical and research work.