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Link to original content: https://link.springer.com/doi/10.1007/s11412-012-9147-y
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The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions

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Abstract

Research related to online discussions frequently faces the problem of analyzing huge corpora. Natural Language Processing (NLP) technologies may allow automating this analysis. However, the state-of-the-art in machine learning and text mining approaches yields models that do not transfer well between corpora related to different topics. Also, segmenting is a necessary step, but frequently, trained models are very sensitive to the particulars of the segmentation that was used when the model was trained. Therefore, in prior published research on text classification in a CSCL context, the data was segmented by hand. We discuss work towards overcoming these challenges. We present a framework for developing coding schemes optimized for automatic segmentation and context-independent coding that builds on this segmentation. The key idea is to extract the semantic and syntactic features of each single word by using the techniques of part-of-speech tagging and named-entity recognition before the raw data can be segmented and classified. Our results show that the coding on the micro-argumentation dimension can be fully automated. Finally, we discuss how fully automated analysis can enable context-sensitive support for collaborative learning.

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Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Empirische Pädagogik und Pädagogische Psychologie, Leopoldstraße 13, 80802, Munich, Germany

    Jin Mu, Karsten Stegmann & Frank Fischer

  2. Universität Koblenz-Landau, Institut Erziehungswissenschaft/Philosophie, Bürgerstrasse 23, 76829, Landau, Germany

    Karsten Stegmann

  3. Language Technologies Institute, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA

    Elijah Mayfield & Carolyn Rosé

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  1. Jin Mu
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  4. Carolyn Rosé
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  5. Frank Fischer
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Correspondence to Jin Mu.

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Mu, J., Stegmann, K., Mayfield, E. et al. The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. Computer Supported Learning 7, 285–305 (2012). https://doi.org/10.1007/s11412-012-9147-y

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