Creating Courses for Adults: Design for Learning (2015), by Ralf St. Clair
Bibliographic record
Abstract
In the book Creating Courses for Adults: Design for Learning, Ralf St. Clair outlines critical elements for effective course design within adult learning contexts using a socio-cultural learning theory lens that is concerned with interactions between learning stakeholders during a teaching and learning process.The author's theoretical perspectives about learning, grounded in the above learning theory, influence his overall approach for the book.He believes that sociocultural values-which recognize shared experiences and the value of situational interactions between teaching and learning stakeholders-should inform all educators' approach to design and learning, just as they do his.At its core, the book speaks to the importance of investing deliberate and sufficient thought and effort into the process of designing and learning for adults.The author believes and stresses that failing to follow this intentional and rigorous approach would significantly undermine and compromise the value, impact and outcome of learning for all stakeholders involved-especially the learner.The nine-chapter text is divided into two parts.The first part consists of three chapters which address what St. Clair considers to be the three key factors that must be taken into account for effective adult course creation: Educator factors (including their experience and learning/teaching philosophy and preferences); learner factors (including learner expectations, personalities and influences during learning); and contextual factors (such as learning location, spaces and environments).According to him, the ability of educators to meaningfully reflect on their teaching philosophy and approach as well as the underlying origins and assumptions is foundational to successful course creation.Combining this first step with a comprehensive understanding and accommodation of relevant learner and contextual factors, as part of the overall learning architecture by the educator, is the main thrust of part one.In the second part, the author dedicates six chapters to unpacking the key categories of decisions that educators should make in relation to the three key factors (i.e.educator, learner and context) of course design expounded on in the first part.The foundational decision for course designers is presented as the ability to determine the objective or purpose of the course from a perspective that both integrates the formal course aims as well as the interests and needs of the learners who will be attending the course.The author stresses that educators should decide on an approach that also reflects the learners' particular interests (at least to a significant degree) as this would better support effective learning and realization of meaningful outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".