Project-Based Second and Foreign Language Education
Bibliographic record
Abstract
Dewey’s idea of Project-based Learning (PBL) was introduced into the field of second language education nearly two decades ago as a way to reflect the principles of student-centered teaching (Hedge, 1993). Since then, PBL has also become a popular language and literacy activity at various levels and in various contexts (see Beckett, 1999; Fried-Booth, 2002; Levis & Levis, 2003; Kobayashi, 2003; Luongo- Orlando, 2001; Mohan & Beckett, 2003; Weinstein, 2004). For example, it has been applied to teach various ESL and EFL skills around the world (e.g., Fried-Booth, 2002). More recently, PBL has been heralded as the most appropriate approach to teaching content-based second language education (Bunch, et al., 2001; Stoller, 1997), English for specific purposes (Fried-Booth, 2002), community-based language socialization (Weinstien, 2004), and critical and higher order thinking as well as problem-solving skills urged by the National Research Council (1999). Despite this emphasis, there is a severe shortage of empirical research on PBL and research-based frameworks and models based on sound theoretical guidance in general and second and foreign language education in particular (Thomas, 2000). Also missing from the second and foreign language education literature is systematic discussion of PBL work that brings together representative work, identifying obvious gaps, and guiding the field toward future directions. This, first of its kind, volume bridges these obvious gaps through the original work of international scholars from Canada, Israel, Japan, Singapore, and the US.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".