Mapping Patterns of Relations in an Online Graduate Course
Bibliographic record
Abstract
This study explores the patterns of relations that emerged and mutated during a particular semester of an online, graduate course, Multimedia Design for Learning. The assemblage, a learning community, was comprised of a professor-course designer, learners, the course content, digital connectivity, a learning management system (LMS), digital media production software, learning tasks, assessment criteria, and emergent activities. We describe the expected and unexpected relational interplays observed among the actors and map the performativity of the learning community. Within this interplay we were more concerned about how particular nodal points (actors within a network) came to operate as sites of attachments (bonds between actors), and simultaneously promulgated different sensibilities and new relations, which in turn, worked to transform material/digital/human objects into agents. Our main interest was to better understand how, from an initially fragile assemblage, an online learning community could emerge, reconstitute, and/or dissolve. We first describe Sørensen’s (2009) patterns of relations (regions, networks, and fluids) metaphor. Then, we consider the shaping, reshaping, and co-constitution of the patterns of relations (Mol & Law, 1994). We also describe the role of obligatory points of passage, and sites of attachment that held the assemblage’s network together. Our methodological approach drew upon Hine’s (2000; 2004) principles for undertaking a virtual ethnographical study. In order to gather our data, we conducted online, structured, asynchronous, text-based interviews with seven of the fourteen course participants. A second data set was derived from the course designer-instructor’s (also a co-author here) reflective notes. As a research-group, we spent reflexive time constructing and applying a guiding conceptual framework for data analysis. We engaged in two rounds of coding. The first round was descriptive; the second round was self-reflective. In this paper, we focus on key themes that describe student-participant’s chosen sites for: 1) finding familiarity/continuity in the processes of navigating synchronous and asynchronous communication channels and associated resources initially chosen by the instructor, (2) finding ways to collaboratively engage in knowledge construction within the course, and (3) circumventing the patterns of relations initially implemented within the course design. We conclude the paper by discussing how initial attempts to create spaces for specific patterns of relations (“design choices”) appeared to evolve within the learning community assemblage; that is, how activities emerged unexpectedly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".