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Record W4239636397 · doi:10.4018/9781878289742.ch003

Policy Processes for Technological Change

2011· book-chapter· en· W4239636397 on OpenAlexaffabout
Richard J. Smith, Brian Lewis, Christine Massey

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAsian Industrial and Economic Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceProcess managementBusiness

Abstract

fetched live from OpenAlex

Universities, among the oldest social institutions, are facing enormous pressures to change. There have always been debates about the university, its purpose, its pedagogical program, and its relationship to other social and political structures. Today, these debates have been given renewed vigor and urgency by the availability of advanced information and communication technologies for teaching and learning. These include computers and computer networks, along with the software and telecommunications networks that link them together. When these technologies are used to connect learners at a distance, they are called “telelearning technologies.” When referring to their use more generally, to include local as well as remote teaching innovations, they are sometimes called “technology mediated learning” (TML). Despite much media attention and recent academic criticism, pressures on universities are facilitated, but not caused, by telelearning technologies. Change in universities is not simply a result of forces acting upon universities, but is the result of a complex interaction of internal and external drivers. The use of telelearning technologies intersects with a host of social, political, and economic factors currently influencing university reform. Technology, in this context, has become the catalyst for change, reacting with other elements in a system to spark a reaction and a change in form and structure. This chapter examines policy processes for the introduction of technology-mediated learning at universities and colleges. It is based on the results of a two-year research project to investigate policy issues that arise with the implementation of telelearning technology in universities and colleges. The focus was on Canadian institutions of higher learning, but the issues raised are common to higher educational institutions in other countries. The study scanned a large number of institutions, reviewed documents, and interviewed key actors including government and institutional administrators, faculty, and students, to discover the range of issues raised by the implementation of telelearning technologies. This chapter discusses these issues and findings.Request access from your librarian to read this chapter's full text.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.456
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.305
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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