Bibliographic record
Abstract
There is a lot of rhetoric related to current internet based distance education as accessible, flexible, just-in-time, cost-effective, innovative and interactive. In particular, discussion about the value of interaction for successful online learning experiences, which is grounded in social constructivist learning theories, has been ongoing for recent decades. The burgeoning popularity of online learning such as a MOOCs phenomenon and the rapid proliferation of its new name “e-learning” have pushed aside the older connotation of distance learning as an inferior form of learning compared to face-to-face instruction. With the advent of web technologies and the growing public interest in the Internet, a simultaneous claim from internet-based research that such environments are inherently interactive has reinforced the rhetoric about the “interactive nature of online learning”. As a result, literature suggests researchers have single-mindedly focussed on developing more effective interactive online learning with neither empirical examination of the claims nor careful investigation of distance educational contexts where their designs would be implemented in. In this context, the changing roles of online teachers have drawn great research attention and so have been conceptualized and theorised. This Foucauldian critical discourse analysis project looks closely into the rhetorical discourse and their influences on instructors’ perspectives and behaviours at open universities to address the gap in our current understanding about distance education. Two foci of this study are i) instructors’ language use: how instructors at open universities talk about their perspectives and experiences of online learning and ii) instructors’ subjects: how each instructor is described and characterized by other members at the universities and why. We conducted semi-structured interviews with 17 instructors in two open universities, one in North America and the other in Asia-Pacific region. Our findings show the powerful impact of the rhetorical discourse on instructors’ perspectives and their subjects, which has increased the potential danger of the institutional abuse of power against or the marginalization of a particular group of instructors. The ultimate aim of this study is not to refute social constructivist assumptions but to provide a different framework to broaden our understanding of the nature of online learning beyond the current set of assumptions.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".