Creating a positive prior learning assessment (PLA) experience: A step-by-step look at university PLA
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
A prior learning assessment (PLA) can be an intimidating process for adult learners. Capella University’s PLA team has developed best practices, resources, and tools to foster a positive experience and to remove barriers in PLA and uses three criteria to determine how to best administer the assessment. First, a PLA must be motivating, as described by the ARCS model. Second, it must enable success. Finally, it must use available resources efficiently. The tools and resources developed according to these criteria fall into two categories: staff and online resources. PLA programs can use both to ensure that all departments provide consistent communication to learners about the PLA process, which will foster a positive experience. The PLA online lab houses centralized resources and offers one-on-one interaction with a facilitator to assist learners step-by-step in the development of their petitions. Each unit contains resources, examples, and optional assignments that help learners to develop specific aspects of the petition. By following the examples and recommendations, learners are able to submit polished petitions after they complete the units. The lab facilitator supports learners throughout the units by answering questions and providing recommendations. When learners submit their petitions, the facilitator reviews it entirely and provides feedback to strengthen the final submission that goes to a faculty reviewer. All of these individuals and tools work together to help create a positive experience for learners who submit a PLA petition. This article shares these resources with the goal of strengthening PLA as a field.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it