Responding To Adult Learners in Higher Education. Carol E. Kasworm, Cheryl J. Polson, and Sarah Jane Fishback. (2002). Malabar, Florida: Kreiger Publishing Company, 190 pages.
Bibliographic record
Abstract
Responding to Adult Learners in Higher Education offers a thoughtful assessment of the unique needs and concerns of today's adult learners.Useful strategies as to how institutions might better meet these needs in more constructive ways are detailed throughout the book.Using a series of demographic and evaluative data, the book begins with an introductory chapter which broadly defines the adult undergraduate student-those students 25 years of age or older-and explores why they are different.While the national statistics on education and socio-economic backgrounds presented here are gathered from American colleges and universities, much of the information contained in this chapter is applicable to the Canadian context.Having defined the adult student, the following six chapters offer higher education institutions substantial but practical advice on how to provide this group with a successful educational experience.There are a number of U.S. universities and colleges who either focus the attention of their institution on, or offer programs designed specifically for, the adult undergraduate student market.These programs have been successful in attracting adult students because they have implemented marketing strategies that clearly understand the adult learner's world.As the authors of this book point out, if a successful marketing strategy is not followed with proper follow-up, particularly in the area of student services, retention may well become an issue.It is critical that universities and colleges find ways for adult learners to conveniently access institutional information that will assist them in attaining their academic goals, including an academic advising process designed to support their changing needs.Another chapter is devoted to the effectiveness of adult learning in a community-based environment.Communities of learning, sometimes referred to as cohort-based learning, result when faculty and staff connect with the complex world of the adult student.Juggling work and family responsibilities along with university studies over an extended period of time, while highly exhilarating, can also be extremely stressful.It is unfortunate that, aside from a brief mention of research by Kasworm and Blowers (1994) that "cohort experiences were judged to be highly effective for both their Jearning and for their psychological well-being" (p.109), the authors give
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".