Cohort Programming and Learning: Improving Educational Experiences for Adult Learners. Iris M. Saltiel and Charline S. Russo. (2001). Malabar, Florida: Krieger Publishing Company, 121 pages.
Bibliographic record
Abstract
Saltiel and Russo's Cohort Programming and Learning gives a detailed account of the practical aspects of cohort-based educational design.As the authors state, the concept of cohort-based learning refers to "a group of individuals who enter a program at the same time, proceed through all classes and academic program requirements together, and complete the program as a group" (p. 1).More than simply grouping learners together, the cohort model involves an established curriculum and bounded group structure, and aims to foster collaborative learning within a collegially supportive group environment.The cohort model is gaining popularity in undergraduate, graduate, and doctoral degree programs that are primarily oriented toward mid-career professionals.The aim of the book is to serve as a resource manual for educators or administrators interested in designing cohort-based programs for adult learners.The book consists of eight chapters.The first two chapters present an overview of the specific features that make the cohort model unique.According to Saltiel and Russo, the cohort model derives its uniqueness from its contained program framework and closed membership.The authors emphasize the community and continuity fostered within the cohort group, as learners share ideas and critical feedback and work to support each other's progress through their shared educational journey.The next three chapters address cohort-based program design, curriculum development, and strategies for teaching and learning.They identiiy a range of considerations involved in cohort-based program planning, from establishing specific educational goals,, to recruiting students and faculty, to selecting appropriate instructional and evaluative techniques.The final three chapters discuss the attributes of cohort learners, implications for practice, and future considerations.The authors identify the intensive group learning experience and the greater certainty of group completion as key strengths of the cohort model, making it "a compelling solution to educational needs today" (p.106).Although Cohort Programming and Learning effectively addresses the practical dimensions of cohort-based program design, Saltiel and Russo write as confirmed cohort "boosters" and their unquestioned support for this educational model contributes to two notable weaknesses of the book.The
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".