Accreditation and Recognition of Prior Learning in Higher Education
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
Abstract The recognition of prior learning (RPL) can, and does, play an important role in the accreditation of higher institutional learning, thereby benefitting students, employers, and society. Using rigorous tools that permit learners to bring forward for assessment their experiential learning from various life experiences, RPL can contribute to a fuller and equally valid expression of learners’ knowledge than does traditional assessment. Additionally, RPL contributes to mitigating issues of equity, diversity, and inclusion in education by acknowledging and valuing a variety of learning opportunities. RPL also raises difficult epistemological issues and the question of knowledge ownership, thus making it a contentious and challenging academic concern This chapter reviews the process and pedagogy of RPL practice within the evolving context of accreditation, both at present and in the future, a future which includes innovations such as open educational practice, MOOCs, and micro-credentialling, all of which create opportunities for traditional modes of accreditation and assessment to re-examine their purpose and process.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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