Individualized Learning in Context: Constructivists’ Teaching Philosophy of Academic Writing for EAL Learners
Bibliographic record
Abstract
The prominent role of teaching philosophy statements is on the rise because they mirror broad skillsets and the expertise of doctoral or master’s program graduates (Merkel, 2020). This paper presents our philosophy of education, of Teaching English to Speakers of Other Languages (TESOL), and of teaching academic writing for English as an Additional Language (EAL) students. First, it is maintained that in education, knowledge is individually and socially constructed (Piaget, 1970; Vygotsky, 1981). In light of the constructivists’ educational philosophy, it is argued that the EAL teachers need to possess knowledge in cognate disciplines to mediate the EAL students’ construction of their individualized linguistic and intercultural knowledge in dynamic, specific learning contexts. Finally, innovative pedagogical suggestions for what to cover and how to deliver the second language (L2) academic writing class with EAL students are offered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".