Discovering indicators of successful collaboration using tense: Automated extraction of patterns in discourse. (19th March 2014) - Thompson, Kate; Kennedy‐Clark, Shannon; Wheeler, Penny; Kelly, Nick
Abstract This paper describes a technique for locating indicators of success within the data collected from complex learning environments, proposing an application of e‐research to access learner processes and measure and track group progress. The technique combines automated extraction of tense and modality via parts‐of‐speech tagging with a visualisation of the timing and speaker for each utterance developed to code and analyse learner discourse, exploiting the results of previous, non‐automated analyses for validation. The work is developed using a dataset of interactions within a multi‐user virtual environment and extended to a more complex dataset of synchronous chat texts during a collaborative design task. This methodology extends natural language processing into computer‐based collaboration contexts, discovering the linguistic micro‐events that construct the larger phases of successful design‐based learning.