The Effects of Integrating Board Games into Ice-breaking Activities in a Fifth-Grade English Class to Reduce Students’ Anxieties
Bibliographic record
Abstract
Elementary-level English teachers in Taiwan were facing problems caused by students’ bimodal distribution of English achievements. Many of the teachers tried new teaching tools to eliminate the obstacles of teaching. This study was based on a quasi-experimental research design and supplemented by qualitative feedback to explore how the new teaching method, ice-breaking board games, could reduce students’ anxieties of learning English. The researchers combined popular board games, Halli Galli and Alles Tomate, with English words and sentences in ice-breaking activities for 10 weeks in two fifth-grade English classes in Taiwan. Forty-eight fifth-grade students were invited to participate in the study and randomly divided into two equal-sized classes. One class was selected as the experimental group, and the other as the control group. The study lasted for 10 weeks. The Foreign Language Classroom Anxiety Scale (FLCAS) developed by Horwitz et al. (1986) was employed as the instrument to measure students’ English learning anxieties. Quantitative data were analyzed by using independent-sample and paired-sample t-tests as well as descriptive statistics, whereas qualitative data (which included the participating students’ feedback and teacher’s reflection notes) were analyzed through content analysis. The research findings suggested that integrating board games in English teaching as ice-breaking activities did not effectively reduce students’ overall foreign language anxiety. However, the descriptive statistics and qualitative data revealed that the constructs of anxiety for both groups had changed in opposite directions, and ice-breaking board games appeared to be more impactful in reducing certain aspects of anxiety construct, namely, communication apprehension.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".