Case Study of a Student with Disabilities in a Vocational School during the Period of Online Virtual Classes due to Covid-19
Bibliographic record
Abstract
In this research we will get an insight into the achievements in mastering foreign languages for one specific student with difficulties. The student with disabilities, the foreign language teacher, the student's mother and the educational rehabilitator participated in the research. The teacher states the support of the inclusive secondary vocational school that the student attends and copes with all challenges. One of the methods used in teaching was mobile learning during the three-month online teaching period of pandemic situation caused by Covid-19. During this period Croatia was forced for the first time in history to implement online teaching. For the purpose of this study all of the students besides this student attended classes in the virtual classroom called Edmodo. Edmodo is the mobile application which is considered as a tool for mobile learning. Mobile learning means that students use their portable devices in order to learn. The interview, used as a method of data collection, was written by participants via email due to the required physical distancing. The materials were then transcribed and a quantitative data analysis was performed. Difficulties in the work of foreign language teachers have been identified, such as lack of experience, lack of teaching assistants as well as lack of additional teaching aids and specific materials and the specific teachers’ training for teaching in the contemporary inclusive school. At the end, the authors suggest guidelines for teachers in vocational schools in adopting new skills and competences in their teaching process in an inclusive school.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".