Changemaking and English Language Learners (Els): Language, Content and Skill Development through Experiential Education
Bibliographic record
Abstract
The idea of offering all children and youth an education that is experiential, student centered, engaging, and relevant to life is not a new concept (Dewey, 1938; Kolb, 1981). Preparing students with the competencies, skills, and character for full participation in the 21st century has become the vision of schools, educators, and organizations around the world (NEA, 2020; Geisinger, 2016; Trilling, B. & Fadel, C. 2009). Changemaker teachers, staff, and administrators believe in facilitating children and youth development as citizens for the 21st century. These educators also guide them as agents of change who empathize with others and solve real life problems for the greater good. These children and youth are what Ashoka calls “Changemakers”. (Ashoka, 2020). This article explores the potential for facilitating the development of English Learners (Els) as Changemakers by using effective Second Language Acquisition (SLA) approaches in combination with experiential approaches. The intent is to contribute a theoretical framework and curriculum ideas for effective practice to help English language learners develop language, access content, and develop 21st century skills as Changemaker attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".