Learning English L2 vocabulary with clickers
Bibliographic record
Abstract
This study explored the use of clickers (i.e., a polling technology) as a tool to promote the acquisition of second language (L2) vocabulary. A growing body of literature on the pedagogical effectiveness of clickers in an L2 context has revealed that clickers can foster learning gains (e.g., Reynolds & Taylor, 2020). However, the extent to which clickers play a role in learning gains compared to other pedagogical approaches lacks consensus; in addition, most research has focused on adult learners and has taken place in large classrooms (Caldwell, 2007). \n \nTo address these limitations, the current research investigated the effects of clickers on L2 vocabulary acquisition in a K-12 educational setting. Two intact groups comprised of 61 Grade 8 students (age range: 13-14) learning English as a second language (ESL) in Montréal (Québec) were assigned to a vocabulary acquisition treatment: while the Clicker Group (CG: n = 31) received instruction via clickers, the Non-Clicker Group (NCG: n = 30) was treated via hand-raising without the target technology. The target vocabulary for the experiment constituted 30 low-frequency words extracted from James and the Giant Peach, a novel by Roald Dahl. \n \nThe pedagogical effectiveness of clickers on participants’ acquisition of the target vocabulary was measured via pretests, posttests and delayed posttests. Overall, the results indicate that the pedagogical use of clickers contributed to L2 vocabulary acquisition, but that the learning gains are comparable in both groups. The discussion of the findings highlights the role of individual differences among members (i.e., some participants improved significantly more than others) and the implications for L2 teaching/learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".