Inner Speech, Dialogue Text and Collaborative Learning in Virtual Learning Communities
Authors: Stefanos Nikiforos, Katia Lida Kermanidis (Ionian University, 2014)
Topic: Learner Corpora and Language Acquisition

Abstract
Virtual Learning Communities offer new opportunities in education and set new challenges in Computer Supported Collabora- tive Learning. In this study, a detailed lin- guistic analysis in the discourse among the class members is proposed in five distinct test case scenarios, in order to detect whether a Virtual Class is a community or not. Com- munities are of particular importance as they provide benefits to students and effectively improve knowledge perception. This analysis is focused on two axes: inner speech and col- laborative learning as they both are basic fea- tures of a community.

Introduction
Virtual Learning Communities (VLCs) constitute an aspect of particular importance for Computer Supported Collaborative Learning (CSCL). The stronger the sense of community is, the more effectively is learning perceived, resulting in less isolation and greater satisfaction (Rovai, 2002; Daniel et al, 2003; Innes, 2007). Strong feelings of community provide benefits to students by increasing 1) the commitment to group goals, 2) collaboration among them and 3) motivation to learn (Rovai, 2002). Virtual Classes (VCs) are frequently created and embodied in the learning procedure (Dillenbourg and Fischer, 2007). Nevertheless there are questions arising: Is every VC always a community as well? How can we detect the existence of a community? What are its idiosyncratic properties? Sharing of knowledge within a community is achieved through shared codes and language (Daniel et al, 2003; Stahl, 2000; Innes, 2007). Language is not only a communication tool; it also serves knowledge and information exchange (Dillenbourg and Fischer, 2007; Knipfer et al, 2009; Daniel et al, 2003; Bielaczyc and Collins, 1999). Communication and dialogue are in a privileged position in the learning process due to the assumption that knowledge is socially constructed (Innes, 2007). Collaborative learning (CL) is strongly associated with communities as it occurs when individuals are actively engaged in a community in which learning takes place through collaborative efforts (Stahl et al, 2006). This active engagement is achieved through public discussion, which is a central way for a community to expand its knowledge (Bielaczyc and Collins, 1999). Developing an understanding of how meaning is collaboratively constructed, preserved, and re-learned through the media of language in group interaction, is a challenge for CL theory (Daniel et al, 2003;Wells, 2002; Warschauer, 1997; Koschmann, 1999). Inner speech (IS) is an esoteric mental language, usually not outerly expressed, having an idiosyncratic syntax. When outerly expressed, its structure consists of apparent lack of cohesion, extensive fragmentation and abbreviation compared to the outer (formal) language used in most everyday interactions. Clauses keep only the predicate and its accompanying words, while the subject and its dependents are omitted. This does not lead to misunderstandings if the thoughts of the individuals are in accordance (they form a community). The more identical the thoughts of the individuals are, the less linguistic cues are used (Vygotsky, 2008; Socolov, 1972). Various works using discourse analysis have been presented in the CSCL field: some of them focus on the role of dialogue (Wells, 2002), others examine the relationship between teachers and students (Blau et al., 1998; Veermans and Cesareni, 2005), while others focus on the type of the language used (Maness, 2008; Innes, 2007), on knowledge building (Zhang et al., 2007), or on the scripts addressed (Kollar et al., 2005). Spanger et al. (2009) analyzed a corpus of referring expressions targeting to develop algorithms for generating expressions in a situated collaboration. Other studies use machine learning techniques in order to build automated classifiers of affect in chat logs (Brooks, 2013). Rovai (2002), examined the relationship between the sense of community and cognitive learning in an online educational environment. Daniel et al. (2003) explored how the notions of social capital and trust can be extended in virtual communities. Unlike these studies, the proposed approach, for the first time to the authors' knowledge, takes into account the correlation between community properties and both inner speech and collaborative learning features (Bielaczyc and Collins, 1999) by applying linguistic analysis to the discourse among class members as a means for community detection. To this end, the discourse of four different types of VCs is analyzed and compared against non-conversational language use.

Conclusion
Applying linguistic analysis to the discourse among the members of a VC can provide us with useful results. Combining the result of the two categories (inner speech and collaboration) we can get strong indications of community existence. Furthermore, results of the analysis can help us improve the design of the VCs. However there is room for future research, e.g. applying this model and evaluating it on a larger corpus and different case studies.