SocialLearn Research Blog

Here in Banff, we’re wrapping up the 1st International Conference on Learning Analytics & Knowledge in the outstanding Banff Centre. I have to say that I’ve also never been anywhere so cold, and this is the only conference where they provide free tissues on every delegate table!

It’s been an exciting event to be at, with a tangible sense that this is only going to get bigger, and fast. More reflections on this later, but here are the slides from the session we ran yesterday, with a focus on how analytics might help us understand not just the quantitative analytics on online discourse (e.g. how many people are engaging in discourse, and how often, etc), but what’s the quality of that discourse? A lot of learning concerns making your thinking visible through the way in which you construct your contributions, whether to a forum, or an essay or article. As confirmed by extensive research, learners have to be inducted into the practices of scholarly writing as defined by their particular discipline – they must learn to show, quite explictly, the line of argument or nature of contribution and how it connects constructively to what has been said previously, either by a peer, or in the literature.

This strand of our work in SocialLearn might go under the heading of discourse-centric learning analytics. In the first approach, we seek to detect quality discourse in textchat, and in the second, we deploy a platform for structured deliberation.

Ferguson, R. and Buckingham Shum, S. (2011). Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat. Proc. 1st International Conference on Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. Eprint: http://oro.open.ac.uk/28955

Abstract. While generic web analytics tend to focus on easily harvested quantitative data, Learning Analytics will often seek qualitative understanding of the context and meaning of this information. This is critical in the case of dialogue, which may be employed to share knowledge and jointly construct understandings, but which also involves many superficial exchanges. Previous studies have validated a particular pattern of ‘exploratory dialogue’ in learning environments to signify sharing, challenge, evaluation and careful consideration by participants. This study investigates the use of sociocultural discourse analysis to analyse synchronous text chat during an online conference. Key words and phrases indicative of exploratory dialogue were identified in these exchanges, and peaks of exploratory dialogue were associated with periods set aside for discussion and keynote speakers. Fewer individuals posted at these times, but meaningful discussion outweighed trivial exchanges. If further analysis confirms the validity of these markers as learning analytics, they could be used by recommendation engines to support learners and teachers in locating dialogue exchanges where deeper learning appears to be taking place. Slides: Learning Analytics & Exploratory Dialogue [PPTX/PDF]

De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. (2011). Discourse-Centric Learning Analytics. Proc. 1st International Conference on Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. Eprint: http://oro.open.ac.uk/25829

Abstract. Drawing on sociocultural discourse analysis and argumentation theory, we motivate a focus on learners’ discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. However, software platforms must gain access to qualitative information about the rhetorical dimensions to discourse contributions to enable such analytics. This is difficult to extract from naturally occurring text, but the emergence of more-structured annotation and deliberation platforms for learning makes such information available. Using the Cohere web application as a research vehicle, we present examples of analytics at the level of individual learners and groups, showing conceptual and social network patterns, which we propose as indicators of meaningful learning. Slides: Discourse-Centric Learning Analytics [PPTX/PDF]