Assessing Adult Learning: A Guide for Practitioners. Joseph J. Moran. (2001). Krieger Publishing Company, Malabar, Florida, 187 pages.
Bibliographic record
Abstract
At first glance, individuals teaching in the field of adult education would pick up Joseph Moran's book, Assessing Adult Learning: A Guide for Practitioners, with great interest.Many literacy practitioners, community instructors, or other educators teaching adults are always seeking ways to assess students and to teach them how to enhance their own learning while still promoting the principles of adult education.Therefore, when adult educators examine the table of contents and read "Understanding the Basic Principles of Informal Assessment," they think: "Finally, oh, good!" Moran promises that informal assessment activities will assist both educators and learners to become involved in the evaluation process.Moreover, he maintains that when educators do this, their students will become more interested and empowered in their learning through the feedback they receive on evaluation.In this second edition, Moran stresses that focusing on practical techniques of assessment is essential.He emphasizes the importance of using a planning grid, similar to that of Angelo and Cross to schedule student assessments.According to Moran, this helps educators integrate learning activities, behaviors, and progress, in relation to thinking, academic and discipline specific knowledge skills, liberal arts values, work preparation, and personal development.Moreover, the grid charts students' progress in learning over time.Furthermore, Moran provides some practical ways to evaluate students through more traditional means, such as test taking while "requiring] learners to display what they know about the task" (p.94) because "the more a learner is assessed for completing a 'real-life' task the more likely it will be considered a performance assessment" (p.95).In addition to discussing how to design traditional tests and performance assessments, Moran highlights how educators may use and assess portfolios to illustrate learners' skills and goal achievements.Before ending the book, Moran explains how to apply assessment holistically to adult basic education, GED, workplace, community education, higher education, continuing professional education, and self-directed learning.Moran concludes this
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.015 |
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".