Incentives and Disincentives for the Use of OpenCourseWare
Bibliographic record
Abstract
This article examines Utah residents’ views of incentives and disincentives for the use of OpenCourseWare (OCW), and how they fit into the theoretical framework of perceived innovation attributes established by Rogers (1983). Rogers identified five categories of perceived innovation attributes: relative advantage, compatibility, complexity, trialability, and observability. A survey instrument was developed using attributes that emerged from a Delphi technique with input from experts in the OCW field. The survey instrument was sent to 753 random individuals between 18 and 64 years of age throughout Utah. Results indicated that the greatest incentives for OCW use were the following: (a) <i>no cost for materials</i>, (b) <i>resources available at any time</i>, (c) <i>pursuing in depth a topic that interests me</i>, (d) <i>learning for personal knowledge or enjoyment</i>, and (e) <i>materials in an OCW are fairly easy to access and find</i>. The greatest disincentives for OCW use were the following: a) <i>no certificate or degree awarded</i>, (b) <i>does not cover my topic of interest in the depth I desire</i>, (c) <i>a lack of professional support provided by subject tutors or experts</i>, (d) <i>a lack of guidance provided by support specialists</i>, and (e) <i>the feeling that the material is overwhelming</i>. The authors recommend that institutions work to transition some OCW users into degree-granting paid programs as well as adopt a marketing campaign to increase awareness of OCW. Additionally, OCW websites should make their content available to recommendation engines such as ccLearn DiscoverEd, OCW Finder, or OER Recommender and should reciprocally link to one or more of these sites.
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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".