Technologies of experience and potential spaces: the implications of digital media for theories of learning
Bibliographic record
Abstract
This dissertation explores new theoretical frameworks for interpreting the significance of digital media for learning, in particular through their experiential and aesthetic qualities. By focussing on the aesthetics of digital media, and specifically computer games and simulations, this study explores the ways in which such media function as technologies of experience rather than objects of representation and, hence, pose new questions about relations of knowledge and learning. While theories of literacy and media education offer a rich literature for understanding the complex issue of visual representation, this study suggests that education needs a theory of digital aesthetics in order to address the unique qualities of the communication of ideas through digital media—experiential qualities such as immersion and interactivity. In order to address the qualities that set digital media apart from other contemporary representational technologies, a theory of digital aesthetics is elaborated in relation to three epistemological and ontological tensions—between our notions of subject and object, real and imaginary, immediacy and mediation. These conceptual tensions are examined through two case studies: first, a limited study of practices in scientific visualization and contemporary art; and, second, a more extensive study of digital games and simulations. These case studies provide a context from which to consider the implications of digital aesthetics for theories of learning through object relations theories of intermediate and cultural experience, or object relations aesthetics: how do we think about learning if digital media are technologies of aesthetic experience? The dissertation concludes with a discussion of theories of learning that privilege the work of the imagination and notions of fantasy and play as the grounds of possibility for the development of the Self. When taken together, theories of digital aesthetics and object relations aesthetics suggest the possibility of new questions for curriculum theory that aim to explore digital media not primarily in terms of their content but as a method for observing how we experience ourselves in the world. As technologies of experience, digital media offer sites of creative illusion and potential spaces for the kind of play or practice of freedom that can help us learn how to learn.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.067 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".