Learning Difficulties in German as a Third Language Experienced by Turkish Undergraduate Students
Bibliographic record
Abstract
The current study aims to investigate learning difficulties in L3 German experienced by the Turkish undergraduate students. The target group involved 29 students from three academic programs at a state university in Turkey: Travel Management and Tourist Guiding, Tourism and Hotel Management and Aviation Management. In the study, semi-structured interview form was designed by the researcher as data collection tool. In the analysis process of the data, the thematic analysis procedure was conducted. The results of the study indicated that the Turkish undergraduate students had learning difficulties in L3 German due to three main factors such as linguistic facts, effective factors and external factors. The linguistic facts involve the learning difficulties related to the linguistic characteristics of German language, linguistic habitus (effect of L2 English) and the effect of L1. In the context of effective factors, it was discussed that the lack of motivation, negative attitudes towards German language, the level of readiness for learning L3 German and the lack of self-confidence lead the students to experience negative learning process in L3 German. Finally, socio-cultural developments and the financial matters emerging in the learning environment were considered as external factors that lead the students to have difficulties in learning L3 German.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".