A Methodology for Understanding Cognitive, Affective, and Cultural Aspects of Learning in Online Discourse

Aims and Significance

            Prior to 1992, content analyses of computer-mediated communication (CMC) were scarce (Mason, 1992). An early exception to the quantitatively-oriented evaluation methods of CMC was Henri’s work (1992) which attempted to qualitatively define the content of online interactions. In the following years, Henri’s work influenced other researchers, such as for example Gunawardena, Lowe, and Anderson (1997) who developed the Interaction Analysis Model (IAM), which acknowledged Henri’s work but identified the model’s foundation in a teacher-centered paradigm as a weakness. Similarly, Garrison, Anderson, and Archer (2001), who were also influenced by Henri’s work, proposed a model for analyzing critical thinking in online discussions.

            While the research community has greatly recognized the importance of the work described above as providing initial frameworks for coding online discussion, criticisms call attention to the lack of robust research methods for analyzing online discourse in computer supported collaborative learning (CSCL).  For example, Wever, Schellens, Valcke, and Keer (2006) argued that even though content analysis is often used in CSCL research, explicit procedures for developing a coding scheme are not made explicit.

            In this paper, we argue that despite recent developments regarding methodological issues in CSCL research, the field is still missing a methodology for discourse analysis, which explains (a) the role of the individual mind in the dialogical process, (b) the role of culture in the social process—an issue mostly ignored in CSCL research, and (c) how affective and cognitive elements affect social interaction in CSCL. In this paper, we discuss a methodology for understanding cognitive, affective, and cultural aspects of learning in online discourse.

Theoretical Framework

            Succinctly, our theoretical model focuses on representations, both inside and outside the head, and shows how individual minds align and share within a distributed process as well as how knowledge and culture are propagated between different minds and artifacts. In essence, this model explains the contribution of individual mind and culture in collaborative learning while at the same time it also provides detailed analysis of the social and collaborative process between people and artifacts. 

Procedure

During a research study in the spring of 2006, 120 undergraduates, 60 from a European country and 60 from the United States were given background materials to read about a political issue. Students, in dyads, were asked to discuss the issue in a text-based CSCL environment. Sixty dyads were randomly formed, 20 Europeans, 20 Americans, and 20 mixed. 

Results

Component 1: The Unit of Analysis

            The unit of analysis was a meaning unit-- a sentence or part of a compound sentence that could be regarded as “meaningful” in itself, regardless of the meaning of the coding categories applied to it. A detailed segmentation procedure, which will discussed in detail during the presentation, was applied to distinguish a “meaning unit”.

Component 2:  Categories of Scoring

            The two primary categories were: (1) Ima = Information present in the materials provided to learners from the background summary, and (2) M(I) = Knowledge brought from the mind of the individual that was not present in the background materials.

            Primary category 2, was comprised of five secondary subcategories: (1) M(C) = Knowledge brought from the mind of the learner that was directly related to the learner’s culture; (2) M(E) = Knowledge brought from the mind of the learner that was emotionally charged; (3) M(Inf) = Knowledge derived from the mind of the learner that was an inference; (4) M(Io)x = Knowledge brought from the mind of a learner, or from the other learner that was not present in the reading materials; and (5) M(Vj) = Knowledge brought or derived from the mind of the learner that was a value judgment, belief, judgment or opinion.

            Subcategory 4 was comprised of two subcategories: (1) M(Io)P = Knowledge brought from the mind of the learner that was a personal experience; and (2) M(Io)NP = Knowledge brought from the mind of the learner that was not a personal experience.

Component 3:  Evaluation of Critical Thinking

            The following criteria were used to evaluate critical thinking in the discourse generated by each dyad:

1.  Analyze the problem by identifying important points of consideration;

2.  Generate solutions/points of view to the problem borne from multiple perspectives;

3.  Develop the reasoning for each solution/point of view;

4. Decide which is the best solution by providing reasons, evidence, and/or alternatives;

5.  Evaluate the clarity, relevance, completeness, and fairness of the thinking process.

            Initially, all transcripts were converted to diagrams. Once a diagram was derived, each component was assigned a value of 1 for its presence. The sum of all was used to evaluate the quality of critical thinking in the transcripts.

            In conclusion, the results revealed that learner representations are saturated with personal/emotional elements that preclude the ability to think critically about a problem. These elements do not transfer from mind to mind if a learner has no prior personal experience with the problem and no cultural context in which to embed problem elements. Artifacts of mind of one learner offer few affordances to another in the absence of cultural synergy.

References

De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review.  Computers & Education, 46(1), 6-28.

Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence,   and computer   conferencing in distance education. American Journal of Distance   Education, 15(1), 7-23.

Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in  computer conferencing. Journal of Educational Computing Research, 17, 397-431.

Henri, F. (1992). Computer conferencing and content analysis. In A. R. Kaye (Eds.), Collaborative learning through computer conferencing: The Najaden papers (pp. 115- 136). New York: Springer

Mason, R. (1992). The textuality of computer networking. In R. Mason (Ed.), Computer Conferencing: The Last Word. Victoria, British Columbia: Beach Holme Publishers  Limited.