Developing an Institutional open educational practices (OEP) Self Assessment Instrument
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As institutions move to considering the implementation of open education environments, it is critical to understand the characteristics and potential success factors for institutional open educational practices. Higher education institutions are changing to meet the needs of contemporary learners, and as a result, there is a need to discuss the benefits and challenges of implementing open education practices in these spaces (Paskevicius, 2017). While there are currently limited institutional case studies on openness to build upon (Morgan, 2018; Childs, Axe, Veletsianos & Webster, 2019), there is potential for the lessons learned from the rich research on blended learning (Lim & Wang, 2017; Graham et. al., 2013) and institutional transformation research (Kezar & Eckel, 2002) to lend insight to potential practices for institutional OEP initiatives. By adopting both an appreciative and critical approach, a draft OEP self-assessment instrument for institutions was created with the intention of examining the similarities and differences between institutional approaches and their evolution. This workshop will provide an overview of the theoretical underpinnings and description of the OEP self-assessment instrument and its component parts. Through small group activities, participants will examine and discuss propositional categories and components of the OEP self-assessment instrument. Participants will complete the online OEP self-assessment instrument and discuss their experience with a focus on expanding their understanding of what others are doing in institutions globally, and improving the OEP instrument for global use. Participants will also identify initiatives and/or approaches that could help expand OEP at their own institutions.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it