Finding student-centered open learning environments on the internet: Automated dialogue assessment in academic virtual communities of practice. (June 2015)

• Record Type: Journal Article
• Authors: Nistor, Nicolae; Trăuşan-Matu, Ştefan; Dascălu, Mihai; Duttweiler, Heather; Chiru, Costin; Baltes, Beate; Smeaton, George
• Is Part Of: Computers in human behavior. Volume 47(2015)
• Page Start: 119
• Page End: 127
• Publication Date: 2015-06
• Subjects: Virtual communities of practice -- Open-ended learning environments -- Natural language processing -- Dialogue analysis -- Polyphony -- Inter-animation; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2014.07.029
• Languages: English
• ISSNs: 0747-5632

Abstract

Highlights: The study analyzes dialogue in virtual communities of practice (vCoP). Learning analytics are employed in the form of automated content analysis. Significant differences were found between central and peripheral vCoP members. Central participants produced and initiated more dialogue of higher quality. The employed tools appear appropriate for assessing open-ended learning environments. Abstract: Starting from the socio-constructivist concepts of (virtual) community of practice (vCoP) and internet-based argumentative open-ended learning environments, this study proposes and validates two tools for automated dialogue assessment, ReaderBench and Important Moments, developed on the ground of the polyphonic social knowledge building model. The analyzed corpus was the dialogue produced by an academic vCoP with N = 179 community members in 23 months, and consisting of 3685 interventions in 292 text-based discussion threads. The analysis results uncovered significant differences in the discussion threads produced by central and peripheral participants, such that central participants produced more interventions with higher collaborative dialogue quality, and the discussion threads they initiated were longer and involved a larger number of participants. Moreover, based on the automated analysis result, the vCoP participants could be classified in two clusters corresponding to the well-known core-periphery structure of CoPs. These findings are consistent with those revealed by other methods, and suggest that the employed tools are appropriate for identifying virtual communities that are appropriate as open-ended learning environments. Further research and development is needed to deepen quantitative vCoP models and test communication strategies recommended to students in vCoP-based argumentative open-ended learning environments.