Analysis of Teachers' Questions in the STEAM Class for Students with Intellectual Disabilities
Bibliographic record
Abstract
The purpose of this study is to analyze the various characteristics of teacher's question to find out whether teachers who teach STEAM classes are conducting effective classes that can stimulate creativity and improve problem-solving skills. For the purpose of the study, the video recording data of the 8th hour of the STEAM class by two special education teachers and the teaching/learning process plan of the class were collected. Then, researchers and research assistants transcribed all verbal interactions that appeared in class and the researcher read the transcription data line by line and analyzed the type of questions. The results of the study are as follows. First, when examining the questions that appeared in the STEAM class of special school teachers for students with intellectual disabilities by question type, closed questions including cognitive & memorial questions and convergent thinking question were very high, and open-ended questions including divergent thinking questions and evaluative thinking questions were relatively low. Second, the stage of STEAM class was divided into introduction, development, and wrap-up stages, and as a result of analyzing the degree of use of question by special school teacher for students with intellectual disabilities according to each stage, the question was used a lot in the order of development, introduction, and wrap-up stage. By analyzing teachers' question in the operation of STEAM classes, it will be possible to prepare basic data for improving science classes using STEAM education in special schools for students with intellectual disabilities.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".