A Study on Using Serious Games in Teaching German as a Foreign Language
Bibliographic record
Abstract
The interest in Digital Game-Based Language Learning (DGBLL) has increased considerably in recent years although being a relatively new approach. Despite the interest that DGBLL took, the studies in the context of German as a Foreign Language (FL) are quite limited. Moreover, DGBLL in the Turkish context is not prevalent. Due to this gap in the literature, a research project was launched at a big state university in Turkey in 2014 to explore the potentials and limitations of DGBLL. The study focuses specifically on serious games for FL teaching and learning. The aim of the project, in addition to the promotion of linguistic skills of the learners, is to contribute to the development of professional qualifications of the future FL teachers. The present research aims to report on the pilot study of the project. A one group pre-test post-test research design was used in the study. Quantitative data was collected via two opinion questionnaires implemented at the beginning and at the end of the process as well as a receptive vocabulary test. Qualitative data was collected via semi-structuted interviews and game diaries that participants kept. Two serious games for German was selected and used with traditional dictation, transcription and reading comprehension activities. The results of an 11-week gaming activity indicated significant differences between pre- and post-tests in vocabulary. Additionaly, age was found to be an important factor that affects participants’ attitudes towards serious games. The results indicate that the participants found game activities useful for the development of other language skills.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".