Review of "Measuring college learning responsibly: Ac- countability in a new era"
Bibliographic record
Abstract
In his book, Measuring college learning responsibly: Accountability in a new era, Shavelson sets out to provide a summary and critical evaluation of creating learning assessment and accountability systems that support the improvement of teaching and learning and at the same time systems that will provide external accountability.He claims that this book presents alternatives to existing methods of learning assessment and accountability systems that aim to significantly improve college teaching and learning and for delivering information to external audiences.He has been developing the ideas for this book with targeted groups such as policy makers in educational, governmental, and public institutions, for almost 20 years.The history of learning assessment and its development is chronologically organized and smoothly transitions into the topic of accountability.A summary of each of the book's chapters follows.In Chapter 1, the policies regarding post-secondary learning assessment are discussed, followed by Chapter 2 where Shavelson explores measuring learning in post-secondary education.In Chapter 3, readers are provided with a history of learning assessment from the 19th century to the present.Here, the author refers the College Learning Assessment (CLA), which he helped to develop.All of chapter 4 is devoted to a more in-depth discussion of CLA.Chapter 5 provides what the author calls "two exemplary campus assessment-of-learning programs".Here, readers are introduced to these programs along with the post-secondary institutions (Alverno College and Truman State College) that host them.The chapter also provides a case study of four universities.Chapters 6 through 10 expand the discussion of the topic of learning assessments and their accountability in post-secondary institutions.Chapter 6 stresses the importance and urgency of increasing the accountability of information in post-secondary institutions.Chapter 7 explains what
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".