Charlotte N. Gunawardena

Introduces a text‑analytics‑based social learning analytic method to automate the quantitative detection of the five IAM levels in online discussions. The approach provides rapid “snapshots” of evolving knowledge construction to support timely instructional adjustments during ongoing collaborations. citeturn19search2

Positions Interaction Analysis (e.g., IAM) as a qualitative approach to assess knowledge construction in online forums and demonstrates how learning analytics and social network analysis can complement it. Using an online discussion dataset, the authors show how combining methods illuminates social dynamics that support knowledge building. citeturn19search0turn19search4

Explores how academics in Sri Lanka, Pakistan, and Mauritius perceive their own cultural contexts and how culture influences online learning. Mixed methods (questionnaires, forum analysis, journals) revealed differing cultural patterns (e.g., high power distance and collectivism in Sri Lanka/Pakistan; less definitive patterns in Mauritius) with practical implications for culturally sensitive online course design. citeturn18search0

Synthesizes studies that applied Gunawardena, Lowe, and Anderson’s (1997) IAM to analyze online discussions. The review affirms IAM’s broad utility across tools and contexts, while also identifying limitations and proposing refinements—such as attending to orchestration of learning—to better capture knowledge construction processes. citeturn15search0

Mixed‑methods study in a multinational corporation’s online program found online self‑efficacy to be the strongest predictor of learner satisfaction and collegial support the strongest predictor of transfer of learning. Courses used problem‑centered, case‑based designs with LMS and multimedia tools; qualitative data contextualized quantitative findings for program improvement. citeturn16search1turn16search2

Proposes a theoretical framework for online communities of practice that leverage Web 2.0 “collective intelligence” tools. Drawing on sociocultural learning theories and action‑research experience, the framework models a community’s spiral from context through discourse, action, reflection, and reorganization toward socially mediated metacognition. citeturn17search0

Based on the GlobalEd inter‑university computer conference, this study tested whether “social presence” predicts learner satisfaction in a text‑based medium. A stepwise regression model indicated social presence, perceived equal opportunity to participate, and technical skills explained most variance in satisfaction, with social presence alone accounting for the majority share. Findings suggest social presence is a strong predictor of satisfaction and that participants often used emoticons to compensate for missing nonverbal cues—implications for designing academic computer conferences. citeturn11search2

This paper proposes the Interaction Analysis Model (IAM) to examine how meaning is negotiated and knowledge is co‑constructed in computer‑mediated collaborative learning. Developed from a global online debate dataset and grounded theory building, the model delineates phases from sharing to integration and application. Applications to further conferences are discussed, highlighting how formats support or hinder movement toward higher‑level synthesis. citeturn11search0turn11search4

Reviews social presence theory and its relevance for analyzing interaction, communication, and collaborative learning in text‑based computer‑mediated communication. Two GlobalEd studies suggest that—even in a medium low in nonverbal cues—participants can perceive CMC as interactive and engaging when moderators cultivate community and effective interaction strategies. Practical guidance is offered for fostering social presence in online conferences. citeturn13search0

Extends Moore’s three‑part interaction framework by introducing learner‑interface interaction—the interaction between a learner and the technology mediating instruction. The article argues for instructional strategies that explicitly develop learners’ interface competencies to avoid technology becoming an independent barrier to learning. citeturn14search7