Analysis of Constructivist, Network-Based Discourses: Concepts, Prospects, and Illustrations

Introduction

Discourse Analysis (DA) is often considered to be situated within a socially constructivist setting, drawing loosely on Vygotsky’s theoretical framework which interprets human inquiry within culturally mediated social interactions and the larger set of practices of learner communities (Imm & Stylianou, 2012). In this respect, it is interesting to note that, according to Scardamalia and Bereiter (2003), constructivism “is a term whose vagueness beclouds important distinctions” between shallow and deep forms of constructivism. They argue that “the shallowest forms engage students in tasks and activities in which ideas have no overt presence but are entirely implicit. Students describe the activities they are engaged in (e.g., planting seeds, measuring shadows) and show little awareness of the underlying principles that these tasks are to convey. In the deepest forms of constructivism, people are advancing the frontiers of knowledge in their community” (Scardamalia & Bereiter, 2003, pp. 1370-1373).

In particular, discourse analysis of mathematics discussions has shown that mathematical knowledge creation (i.e. “deep constructivism”) can be facilitated by encouraging learners to “justify their ideas, evaluate one another’s ideas carefully, and ask questions” (Chen, Chiu, & Wang, 2012, p. 868). This finding is consistent with our previous empirical research which was based on peer reviewing-based learning, facilitated by e-learning technology, such as the Compendium Platform (Poelmans, Wessa, Milis, & van Stee, 2009), that we developed over the last few years to organize statistical courses (Wessa, De Rycker, & Holliday, 2011).

Notwithstanding the widespread adoption of computer-assisted learning approaches, there seem to be only few studies in which computer mediated communication is actually studied in terms of structure, content, and quality of learner messages (Lu, Chiu, & Law, 2011). The chapter attempts to fill some of the gaps in the literature and discusses three important aspects of DA based on social learning networks in statistics education: (a) the provisioning of innovative e-learning technology encompassing reproducible statistical computations, collaborative writing, and peer reviewing; (b) the capturing and use of objectively measured meta information about student discourses and associated social interaction networks using the e-learning technology; (c) several methodological concepts that allow researchers and educators to better understand the discourses and underlying social dynamics in collaborative, constructivist learning.