K-12 content teachers designing language tasks: A follow-up to Erlam, 2016
Bibliographic record
Abstract
This study, a follow-up to the research reported by Erlam, investigated K-12 content teachers’ ability to design tasks for content instruction in U.S. schools. Thirty-nine K-12 teachers who were enrolled in an English as a second language (ESL) teaching methods course participated in the study and each designed two language learning tasks. Two researchers rated the tasks following Ellis and Shintani’s four task criteria, as in Erlam. The study addressed the questions: (1) How successful are content teachers in designing tasks that satisfy Ellis and Shintani’s criteria? (2) Which of the criteria do teachers find easiest and most difficult to satisfy? and (3) Do the teachers improve in meeting the criteria in a second round of task designs? Ninety-two percent and 95% of the tasks satisfied three of the four criteria in task designs 1 and 2, respectively. The teachers excelled most at creating contexts for meaningful communication (92%) and including an information gap (92%) in their first tasks. Incorporating a clearly defined outcome was the most difficult criterion (66%) for teachers to achieve. There was no significant improvement from Task 1 to Task 2 in successfully incorporating the four criteria. The content teachers incorporated more of the task features than the foreign language teachers in Erlam. Over 90% met the majority of the criteria, compared to 82% in Erlam’s study. Another important difference was that participants in Erlam’s study found the easiest criteria to address was including an outcome, and they struggled most to allow learners to use their own linguistic resources and incorporate a gap. The content teachers in the follow-up study struggled most to include an outcome, but consistently incorporated communication gaps and grew in their ability to ensure that learners use their own linguistic resources. This suggests that language teachers may focus more readily on language forms, while content teachers focus more on meaningful content than on language, providing support for learners to focus on meaning.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".