Academic journal article Quarterly Review of Distance Education

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

Academic journal article Quarterly Review of Distance Education

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

Article excerpt


Asynchronous discussion forums have become the main vehicle through which the teaching learning process in online courses is facilitated. Even massive open online courses are incorporating discussion forums to enhance interaction in otherwise teacher-directed and teacher-designed online courses. Yet the question remains as to how learning within these discussion forums can be deciphered and understood. New methodologies such as learn-. ing analytics have provided means to analyze large sets of data from online courses. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ("big data") in order to discover the factors that contribute to learning. We explore how the process of learning, especially the process of knowledge construction in online asynchronous discussions, can be mapped, analyzed, and understood using approaches such as interaction analysis and learning analytics. We discuss these methods of analyses from our own personal perspectives and experiences analyzing transcripts of a computer discussion where students engaged in collaborative learning.

Interaction Analysis and Learning Analytics

Jordan and Henderson (1995) observe that interaction analysis approaches view learning as a distributed, ongoing social process, in which evidence that learning is occurring or has occurred must be found in understanding the ways in which people collaboratively engage in learning. Interaction analysis looks at "the interaction of human beings with each other and with objects in their environment. It investigates human activities, such as talk, nonverbal interaction, and the use of artifacts and technologies" (Jordan & Henderson, 1995, p. 39). Thus, interaction analysis considers interaction as a function of the reciprocal influence among human beings, objects, and their environment.

The Interaction Analysis Model (IAM) was developed by Gunawardena et al. (1997) to qualitatively examine these interactions (or discussions) during the phases of knowledge construction. The IAM was employed to examine the interaction that occurred in an online global debate to determine whether knowledge was constructed within the group through talk and dialogue, and whether participants changed their understanding or developed new knowledge as a result of group interaction. Based on social constructivist theory (Vygotsky, 1978), the model describes five phases of knowledge coconstruction: sharing and comparing constitute Phase I; dissonance is the focus of Phase II; negotiation and coconstruction comprise Phase III, testing tentative constructions is incorporated in Phase IV, and statements and application of newly constructed knowledge are at the heart of Phase V. See Figure 1 for a detailed description of the IAM, including numerous operations for each of its five phases. The model itself serves as a framework that defines social construction of knowledge as a function of interaction, which is understood as reciprocal cognitive influence among individuals.

The function of interaction in the process of knowledge construction can be further explored by newer methods such as learning analytics. There are many definitions of learning analytics (Long & Siemens, 2011, p. 34), but common to them all is the application of tools and methods for extracting, analyzing, and visualizing learner data. The scientific goal is to understand the factors, structures, and processes of learning. The applied goal is to leverage this understanding to design cognitive artifacts that transform the learning process in a beneficial way. The field of learning analytics has grown with the need to analyze large sets of data on learning generated by massive open online courses, informal learning environments, and open social media platforms like Twitter, where people can interact with thousands of other people distributed globally.


Previous Research Using the IAM

The IAM has been used globally to analyze online discussions. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed


An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.