Globally Networked Learning Environments: Reshaping the Intersections of Globalization and E-Learning in Higher Education
Bibliographic record
Abstract
As many e-learning scholars have emphasized, e-learning -situated in a global network of digital technologies -has, of course, a complex global dimension that manifests itself in diverse ways in different institutional, disciplinary, national, and other local academic and educational traditions as digital technologies intersect with local educational practices, policies, and pedagogies.Accordingly, many e-learning scholars have placed this global dimension of e-learning and its local manifestations at the heart of their scholarship, with Lam (2009), for example, examining the literacy practices of immigrant teenagers in online environments; Al-Fadhli (2008) exploring the perceptions of e-learning at Kuwait University; and Marumo et al (2009) studying the role of an elearning platform for educational innovation in Botswana.Others have compared information and communication technology knowledge and usage through the lens of gender and class in Ghana (Kwapong, 2009); studied the role of e-learning in an early childhood programme offered at a virtual university in Africa (Pence, 2007); examined the role of new literacies in the teaching of writing in Canada (Hammet et al, 2007); or studied the diffusion of e-learning in sub-Saharan Africa (Beaudoin, 2007).This special issue extends this work on the global and local dimensions of e-learning by focusing on the unique nature of this dimension at the intersection between e-learning and globalization in higher education.Although highly complex in any educational, institutional, organizational, or informal setting, in higher education, this intersection takes on a unique significance because higher education has been most directly influenced by corporate-driven globalization policies and trade agreements, most importantly, perhaps, the General Agreement on Trade in Services (GATS) by the World Trade Organization.This dominant corporate-driven approach to the globalization of higher education has been based on a vision of e-learning as a new opportunity for the expansion of global markets for existing industrial-model, locally produced higher education courses and programmes to be repackaged for global delivery and global trade online, whereby that delivery has been envisioned in a rather unidirectional way -from Western, mostly English-speaking universities to everywhere else (see, for example, Bassett, 2006; Marginson, 2007;Peters, 2007;Knight, 2008).As a result, hundreds of millions of public and private dollars have been spent on global Internet-based higher education marketing consortia, many of which have since failed (Marginson, 2004).As Marginson (2004) noted, these original dominant approaches to the globalization of higher education relied largely on reproducing established institutionally bounded courses in digital environments, lacked cultural sensitivity, and disregarded the need for pedagogical innovation to re-envision learning in a globally networked world.At the same time as institutions attempted to reproduce established practices in online courses, many faculty in higher education began to pursue alternative visions of learning,
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| 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".