Development and Assessment of a Training Module on Intellectual Property Literacy
Bibliographic record
Abstract
This paper describes an online, asynchronous learning module on intellectual property (IP) awareness. The learning module described in this paper is the first of a planned set designed as online asynchronous learning activities to give students the foundational knowledge required to practice and develop associated skills to understand the IP landscape and identify and recognize potential new IP opportunities. The first learning module, titled “IP Literacy,” provides an introduction to IP topics including patents, trademarks, copyrights and creative commons, trade secrets, industrial design, and collaboration; and includes curated videos by experts, plus valuable additional tools and resources. The design of the first module was based upon input from capstone course instructors, students and alumni and has undergone a process of pilot-testing and revision. Based on this feedback, the IP Literacy module was designed in the summer/fall of 2021 and was tested with a small population of undergraduate students to refine the content. A pilot offering to one discipline of students took place in the fall 2021 term, before expanding to additional groups in winter 2022. Survey feedback collected from fall 2021 and winter 2022 pilot offerings of the module was generally positive, with the majority of students agreeing that they learned something, and that the module was relevant to their discipline. This paper will summarize this development and pilot-testing process and discuss the next steps for the project.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".