Development of Candidate Teachers’ Problem Solving Ability With the Audience Response System
Bibliographic record
Abstract
In this study, the Audience Response System was investigated as an additional tool for interaction, and its effects on the educational environment were examined. The system was implemented at the Faculty of Sports Sciences of Trakya University in the fall semester of the 2019-2020 academic year. A pre-test of 20 questions, which was asked in the educational sciences section of the public personnel selection examination and had a similar item difficulty index, was applied to the experimental and control groups prior to the implementation of the ARS. Then, the experimental group was asked to solve the educational sciences questions with the help of the ARS-supported lectures, which were delivered 4 h a week for a total of 16 h. The same implementation was imposed on the control group without the ARS support and with the classical recitation method. A post-test of 20 questions with a similar item difficulty index was administered to both groups after this test. Data were analyzed using the SPSS 25.0 package program. A t-test was used to determine the differences between the arithmetic mean of the pre-test and post-test scores of the students. Because the unequaled control group method was used in the experiment design, the “ANOVA for Repeated Measurements” was used for intragroup and intergroup comparisons. In conclusion, it was determined that the implementation of interactive interaction technologies in the educational environment will capture the interest of students and amplify their motivation levels. The results of the study support the conclusion that the ARS system stimulates the sensory organs in terms of understanding the subject, thereby increasing the level of learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".