Bibliographic record
Abstract
As we move towards Precision Medicine, we should also move towards Precision Education, using data science approaches to improve our processes and learner outcomes. This is the purpose of PiHPES: Precision in Health Professional Education Scholarship. This is consistent with the general call-to-arms as presented in the provincial document on Precision Health for Alberta. Central to this project is the extensive use of activity metrics across a range of educational platforms, applications and services. Activity streams are not new and are central to data science where you want to assess what your users do and how your products perform. In education, there have been previous attempts to track learner progress across applications. The most well known is SCORM, which proved to be both rigid and limited, with little more data on user performance than a course pass mark. PiHPES is focused on a more granular approach to activity metrics based on Learning Record Stores (LRS) and the xAPI protocols. Overall, a lot has been achieved in PiHPES, with many of its deliverables and milestones successfully passed. This will be summarized below. The most striking conclusion from all that happened is that the University of Calgary demonstrates a worryingly low state of organizational readiness to apply data science principles and techniques in educational programming. We have not widely publicized PiHPES or its resulting outputs because of several limiting factors. These will also be detailed below but can be summarized as lacking a nice simple example of how this approach works. While we have made a lot of progress, there is not a simple one-pager that we can point to that shows the advantages to a busy educator. We have successfully implemented most of the applications, tools and integrations that we had planned. We are able to store a range of activity metrics in several Learning Record Stores. We have been able to link between these LRSs and conduct useful analyses that were previously unavailable from a single application. This approach, using data science principles, has huge potential for improving educational processes and outcomes. We are keen to explore such analysis further with interested user groups. This will enable us to flesh out the usability and utility aspects of PiHPES. Those who are interested in looking at combining clinical data with educational data will want to look at this following section, where we have developed some unique approaches.
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 | 0.024 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.302 | 0.188 |
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".