Case Study I - Policy Processes for Technological Change
Bibliographic record
Abstract
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. CASE Questions • What policies or processes are in place to guide change in colleges and universities? Who knows about these policies and participates in them? • What are the forces behind technological change in higher education organizations? Are they external or internal? • Can technology be used as a tool for achieving meaningful and positive change or is it an end to itself? • In what ways can technology be used to increase access to education?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 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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".