Bibliographic record
Abstract
The Learning in Depth project, or LiD, is a recent attempt by Kieran Egan to address a widespread educational problem by way of a simple classroom-based project.Students suffer from a breadth of what can only loosely (and very generously) be called knowledge and a lack of depth of knowledge.The basic premise of the project that addresses the problem is: students will receive a topic from a pre-selected list that they will research in-depth to develop some expertise in something, and along the way develop skills, habits, and dispositions that only learning in depth can provide.While students can begin the project at any point, the intention is that students will receive their topics in their first year of school and continue developing expertise in this area throughout their K-12 years.As with Egan's other book, Getting it Wrong From the Beginning: Our Progressivist Inheritance from Herbert Spencer, John Dewey, and Jean Piaget (2002), Learning in Depth begins by framing the educational problem that he aims to solve, "'The kids these days' know nothing" (p. 1).Unlike Getting it Wrong, however, the problem is briefly and succinctly described, and the majority of the book is dedicated to the practicalities of implementing the solution.Egan's major concern is that students are not learning anything in the regular curriculum that they find valuable or relevant enough to remember; thus, they are not truly learning anything.Egan's premise is that the breadth of topics in public school curricula is taught at the expense of the depth required for critical thinking to truly occur.He frames this argument in terms of how society defines an educated person and posits that this is an essential component of learning that is not being reached in schools.An educated person must have both depth and breadth of knowledge.Egan goes on to argue that the depth of knowledge that one accumulates on one topic spans far beyond that one topic, providing access to the nature of knowledge itself."With regard to the knowledge we learn in breadth, we rely always on the expertise of others; when learning in depth, we develop our own expertise.It is assumed that learning something in depth carries over to a better understanding of all our other, 'breadth,' knowledge."(p.6, emphasis in original).Although LiD is an addition to rather than a replacement for the regular curriculum that is primarily student-driven and takes up minimal class time, it appears to offer benefits in every aspect of a child's learning.This approach brings to mind everyone's favourite educational catch-term: critical thinking.From kindergarten to postsecondary education, many educators hear the argument that content is secondary and what students really need to learn are critical thinking skills that they will then
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".