Effective Models for Assessing the Costs of Educational Technologies
Bibliographic record
Abstract
Abstract Better information about how to use new technologies more effectively in higher education is essential, especially given the current rapidly changing environment in higher education. This paper summarizes the results of two field tests of cost assessment models at Washington State University (1) a focused project using the Flashlight Cost Model to analyze three technology strategies along the dimensions of cost efficiency, pedagogical effectiveness and flexibility; (2) a study of the costs of designing, developing, and delivering distance courses delivered on the WWW using new technologies. The study was conducted using the Technology Costing Methodology developed in conjunction by the WCET and NCHEMS. The information each model provides has given WSU key insights into new educational technologies. Introduction Since ignoring new technologies in higher education is not an option, educational institutions across the country are now searching for effective strategies for investing in new technologies and analyzing the cost of courses which use the new technologies. No strategy promises to be cheap. The challenge is to find or develop approaches that are effective relative to cost. This paper outlines Washington State University's (WSU) experience field testing two models designed specifically to analyze costs, especially costs of new technologies in education. The first model tested was the Flashlight Cost Model which was developed by the Flashlight Program of the Teaching, Learning and Technology Group of the American Association of Higher Education. The second model applied was the Technology Costing Methodology (TCM) model. The National Center for Higher Education Management Systems (NCHEMS) is participating with the Western Cooperative for Higher Education (WCET) to develop the Technology Costing Methodology. The Project will produce a costing methodology (and related procedures) for calculating technology costs both within an institution and across institutions. Both models apply an accounting concept called Activity-based Costing (ABC) to analyze new technologies and/or delivery techniques in education. Related Works The work summarized in the following paper relies heavily on two handbooks, The Flashlight Cost Analysis Handbook - Version 1 (Ehrmann and Milam, 1999) and a preliminary draft of the Technology Costing Methodology Handbook (Jones, 2000). The Flashlight Cost Model handbook is available for a small fee. At the time of this writing, a second edition was in process. Information about the model can be found from the TLT Group web site: http://www.tltgroup.org/. The TCM manual was in the process of being finalized at the time this article was submitted for review. When the TCM Manual is completed it will be available on the World Wide Web. The following URL should contain information about the manual: http://www.wiche.edu/telecom/Projects/tcm/index.htm. The paper below summarizes some of the differences between the two handbooks. However, the authors experience indicates that the handbooks compliment each other very well. The WCET plans to make available a casebook as well as the cost handbook summarizing field tests by institutions from around the U.S. The casebook, in addition to the Flashlight handbooks, when completed, will contain about 20 case studies. These case studies should provide potential users very valuable insights into cost studies that adapt ABC. It is interesting that most of the applications of activity-based costing to education are occurring in the English-speaking world, specifically; Australia, Canada, Great Britain, Hong Kong and the U.S. An excellent review of cost models can be found in a study conducted at Sheffield-Hallam University (Bacsich, et. al., 1999). The focus of the study was to determine the hidden costs in networked learning. The study is extensive (100 pages) and valuable' in that it provides an excellent literature review and bibliography and contains other interesting material such as case studies, survey results, etc. …
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".