Benchmarking 10 Major Canadian Universities at the Divisional Level: A Powerful Tool for Strategic Decision Making
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Notice bibliographique
Résumé
Introduction (1) It has taken almost 30 years for universities to borrow from the corporate world and integrate the concepts, methodologies, and logistics of various quantitative and qualitative evaluative processes (such as evaluation, assessment, and total quality management [TQM]) into institutional planning. It has taken even more time--beginning circa 1980--for performance indicators, strategic planning, benchmarking, and ranking to gain broad acceptance. A rapid review of the recent history and evolution of benchmarking illustrates the exponential growth of its use to compare and rank universities: more than 40 countries (2) now have national or regional university rankings, including * America's Best Colleges published by U.S. News & World Report (see, for example, U.S. News & World Report 2010). * Maclean's University Rankings produced by the Canadian magazine Maclean's (see, for example, Dwyer 2008). * The University Guide published by the Guardian in the United Kingdom. * The CHE University Rankings produced by the Centre for Higher Education Development in Germany. * Six other international university ranking and league tables systems that compare and rank world universities, such as the Academic Ranking of World Universities produced by the Shanghai Jiao Tong University in China and the World University Rankings edited by The Times Higher Education Supplement in the United Kingdom. Very clearly, a change has occurred in university culture: benchmarking is now widely used throughout the world. This cultural innovation necessarily has affected university institutional research activities. At one time, institutional research offices simply produced facts and figures that were collected and published as a fact book, primarily for descriptive purposes. Starting in the early 1980s, data and metrics began to be related to other purposes such as quality improvement, strategic planning, and accountability. These data were then compared to metrics produced by peer institutions. Benchmarking has since contributed to more policy-oriented institutional research studies and has demonstrated the rich possibilities for the use of data analysis and reporting. It was in this context that a consortium of 10 Canadian research-intensive universities launched a data exchange program in 1999 to share information that could be used to identify and evaluate the best practices of each institution and to help each institution position itself strategically to achieve its mission. One part of the program was devoted to collecting departmental-level academic data (instructional and financial) from these 10 institutions. This project built on two previous studies by the consortium that were experimental and limited in focus. In 2001-2002 and 2002-2003, data for six and 12 academic departments, respectively, were collected. In 2003-2004, the goal was more comprehensive: between 30 and 35 academic departments (figure 1) were benchmarked using 24 variables (figure 2) in comparisons based on selected indicators. This article presents the data from 2003-2004 as a case study to illustrate the purpose and methodology (process, variables, indicators, and ratios) of benchmarking. In addition, the article presents the results of this exercise and describes the multiple uses made of the data generated by the program. Figure 2 Variables and Definitions Section 1--Faculty FTE Tenured/Tenure-Track Faculty: Full-time and part-time (converted to FTE) tenured and tenure-track faculty from all funding sources. Filled positions only. Joint appointments have to be prorated. Individuals with duties outside the department such as vice presidents, deans, and associate deans should be excluded for the duties they assume outside the faculty/department but should be prorated for their work within the faculty/department. The count should exclude non-tenured/tenure-track staff. …
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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 | 0,002 | 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 découle