Case Study I - Policy Processes for Technological Change
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».