Evaluating NTU’s OpenCourseWare Project with Google Analytics: User Characteristics, Course Preferences, and Usage Patterns
Bibliographic record
Abstract
As freely adoptable digital resources, OpenCourseWare (OCW) have become a prominent form of Open Educational Resources (OER). More than 275 institutions in the worldwide OCW consortium have committed to creating free access open course materials. Despite the resources and efforts to create OCW worldwide, little understanding of its use exists. This paper reports OCW project development at National Taiwan University (NTU) and investigates its use with Google Analytics. Reports include strategic plans to overcome challenges to OCW creation and implementation, the project’s growth and maturation, overall use of OCW, and possible future directions. As a result of its 5-year development and of lessons learned, the NTU-OCW experience features: (1) integrating resources on campus and established operating procedures, (2) setting course selection criteria featuring the strength of NTU and Taiwan, (3) providing coherent program support to enhance faculty participation, and (4) adhering strictly to the Creative Commons license. Data from Google Analytics was reviewed for better understanding of the use, characteristics, course preferences, and behaviors of NTU-OCW users. Results show visitors were primarily lifelong learners (65%) in informal learning settings. Statistics indicate an overall successful use of NTU-OCW for Chinese speaking users, especially in urban areas where information and communication technology is more developed. Potential impacts and future improvements are discussed, including how to promote usage of OCW courses for on and off campus users, adding rating features and indexing for customizing search, and integrating OCW into the learning management system (LMS) as part of OER.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".