Layering at all Levels: Integrating Layered Curriculum in Postsecondary Education
Bibliographic record
Abstract
Calls to increase active learning, an approach that positions students in the center of their learning experience, have increased considerably in recent decades. In response, there has been substantial work to expand our understanding and implementation of active learning approaches in many educational spaces. However, much of this instructional design has concentrated on elementary and secondary learning levels, with less development of practice and scholarship focused on active learning in postsecondary education (PSE). Layered curriculum (LC) (Nunley & Evin Gencel, 2019) is an approach to active learning, as well a form of differentiated instruction (DI), that offers students innovative ways to engage with and demonstrate their learning. The model includes three layers of learning, each with its own group of learning activities and assessments, that guide a student’s progression from foundational to more complex engagement with a subject. While the use of this approach is less frequent in PSE, and discussions and evidence of its implementation are limited in the PSE literature, this paper will explore why the integration of LC would especially benefit postsecondary learners. The challenges to its integration in PSE will be addressed, including mitigation strategies, and the importance of collaborative curriculum design.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".