Emergent learning and interactive media artworks: Parameters of interaction for novice groups
Bibliographic record
Abstract
Emergent learning describes learning that occurs when participants interact and distribute knowledge, where learning is self-directed, and where the learning destination of the participants is largely unpredictable (Williams, Karousou, & Mackness, 2011). These notions of learning arise from the topologies of social networks and can be applied to the learning that occurs in educational institutions. However, the question remains whether institutional frameworks can accommodate the opposing notion of “cooperative systems” (Shirky, 2005), systems that facilitate the creation of user-generated content, particularly as first-year education cohorts are novice groups in the sense of not yet having developed university-level knowledge. This paper theorizes an emergent learning assessment item (Flickr photo-narratives) within a first-year media arts undergraduate education course. It challenges the conventional models of student–lecturer interaction by outlining a methodology of teaching for emergence that will facilitate student-directed and open-ended learning. The paper applies a matrix with four parameters (teacher-directed content/student-directed content; non-interactive learning task/interactive learning framework). This matrix is used as a conceptual space within which to investigate how a learning task might be constructed to afford the best opportunities for emergent learning. It explores the strategies that interactive artists utilize for participant engagement (particularly the relationship between the artist and the audience in the creation of interactive artworks) and suggests how these strategies might be applied to emergent generative outcomes with first-year education students. We build upon Williams et al.’s framework of emergent learning, where “content will not be delivered to learners but co-constructed with them” (De Freitas & Conole, as cited in Williams et al., 2011, p. 40), and the notion that in constructing emergent learning environments “considerable effort is required to ensure an effective balance between openness and constraint” (Williams et al., 2011, p. 39). We assert that for a learning event within a Web 2.0 environment to be considered emergent, not only does there need to be an effective balance between teacher-directed content and student-directed content for knowledge to be open, creative, and distributed by learners (Williams et al., 2011), but there also need to be multiple opportunities for interaction and communication between students within the system and that these “drive the emergence of structures that are more complex than the mere parts of that system” (Sommerer & Mignonneau, 2002, p. 161).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".