Abstract
Although participants’ involvement in chat environments has been studied in previous systems, as mentioned in Overview of Empirical Studies, ReaderBench has brought a series of remarkable improvements in terms of collaborative learning:
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Emphasis and better support of the dialogical and polyphonic model previously proposed in PolyCAFe with new visualizations and evaluation factors.
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Refinement of the initial collaboration assessment model (Trausan-Matu et al. 2012b; Dascalu et al. 2010a) based on the social knowledge-building effect, through the use of the cohesion graph (Trausan-Matu et al. 2012a; Dascalu et al. 2013b).
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A novel collaboration evaluation model based on the overlapping effect of voices seen as semantic chains (see 7.5 Dialogism and Voice Inter-Animation) pertaining to different participants.
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The validation of the evaluation mechanics on a long-term discussion group, seen as an aggregation of multiple threads across a longer timespan, and not only the assessment of individual chat conversations (Nistor et al. 2013a; Nistor et al. 2013b, submitted; Nistor et al. 2013c).
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Dascalu, M. (2014). ReaderBench (3) - Involvement and Collaboration Assessment through Cohesion and Dialogism. In: Analyzing Discourse and Text Complexity for Learning and Collaborating. Studies in Computational Intelligence, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-03419-5_9
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