Discovering indicators of successful collaboration using tense: Automated extraction of patterns in discourse

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.

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