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

• Autores: Kate Thompson, Shannon Kennedy-Clark, Penny Wheeler, Nick Kelly
• Localización: British journal of educational technology, ISSN 0007-1013, Vol. 45, Nº. 3, 2014, págs. 461-470
• Idioma: inglés
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• Resumen

  • 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. [ABSTRACT FROM AUTHOR]