Rethinking OER and their use: Open education as Bildung
Bibliographic record
Abstract
Despite the recent increases of interest in open education, notably in massive open online courses (MOOCs) (Fini, 2009), it has been continuously asserted that this form of social knowledge production lacks a philosophical or theoretical foundation (Vandenberg, 1975). Similar accusations have been made with respect to distance education, such as being slow to engage with critical debates in theory and research (Evans & Nation, 1992). In a similar vein, Danaher, Wyer, and Bartlett (1998) claim that researchers in open and distance learning tend to draw on too narrow a range of theoretical resources in their research. Given the considerable rise of open education over recent years, these critical appraisals urge us to expand theoretical approaches and refine our understanding of evolving pedagogical and technological relations (cf. Bell, 2011). In this paper, we contribute to debates surrounding open education and open educational resources by introducing the concept of Bildung (self-cultivation, self-realization) as a powerful reflective tool and framework for approaching open education. We will elaborate on the potentials of Bildung by reviewing the history of the concept and exploring the extent to which Bildung can provide open education with a theoretical framework. Our focus is not exclusively on open educational resources (OER): We follow other commentators (Mackey & Jacobson, 2011, p. 62; cf. Weller, 2011) who argue that ‘openness’ in education necessarily shifts the focus from content (OER) to practices (OEP) that are necessary for the use of that content. We also argue that the beliefs and values associated with Bildung – including autonomy, critical reflection, inclusivity, and embracing the potential for self-development – are suitable for providing a theoretical framework for open education as well as providing a critical lens through which to assess contemporary models of education (e.g., Liessmann, 2006). <!-- @page { margin: 2cm } P { margin-bottom: 0.21cm; direction: ltr; color: #000000; line-height: 130%; text-align: justify; widows: 0; orphans: 0 } P.western { font-family: "Frutiger LT Com 45 Light", "Arial Unicode MS"; font-size: 11pt; so-language: en-GB } P.cjk { font-family: "SimSun", "宋体"; font-size: 11pt; so-language: zh-CN } P.ctl { font-family: "Lucida Sans", sans-serif; font-size: 12pt; so-language: hi-IN } A:visited { color: #800080 } A.western:visited { so-language: de-DE } A.cjk:visited { so-language: zh-CN } A.ctl:visited { so-language: hi-IN } A:link { color: #0000ff } -->
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.019 | 0.044 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".