<i>Make Words Click!</i> Learning English Vocabulary with clickers: users’ perceptions
Bibliographic record
Abstract
Clickers are hand-held devices that wirelessly transmit student input to a computer: students answer multiple-choice questions using their clickers and the answer distribution is displayed on a screen. Previous studies suggest that the pedagogical use of these devices may contribute to learning and that they are positively perceived by students in general and second language education. Despite these optimistic outcomes, clicker studies remain scarce in L2 education and in K-12 contexts.This study investigated 61 adolescent students’ and their teacher’s perceptions of using clickers to learn vocabulary in an English as a Second Language context. Two intact groups of students were assigned to a treatment group (Clicker Group, n = 31; Non-Clicker Group, n = 30). Their perceptions were examined via surveys and interviews, guided by four measures: Learning, Self-assessment, Engagement, and Interactivity. The results suggest that students in the Clicker Group had significantly more positive perceptions than those in the Non-Clicker Group for most measures. This corroborates previous findings regarding students’ perceptions of clickers. Interviews were conducted to assess the teacher’s perceptions. In contrast to the students, the teacher’s perception was predominantly neutral to negative, contradicting existing literature.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".