Interactive Response Systems (IRS) Socrative Application Sample
Bibliographic record
Abstract
In globally developing education system, technology has made instructional improved in many ways. One of these improvements is the Interactive Response Systems (IRS) that are applied in classroom activities. Therefore, it is “smart” to focus on interactive response systems in learning environment. This study was conducted aiming to focus on using Socrative application as a feedback agent among IRSs. The study mainly focused on how could Socrative program as a smart feedback agent be effective in fostering students’ learning. Additionally, students’ responses were examined to have an overall sense of a digitally supported learning period. The study was designed on action research. The research was conducted with 53 junior year students who were prospective teachers in different fields at the same time. In order to obtain, 11 item-survey was developed by the researchers to realize how Socrative program could contribute to reinforce learning in detail. Besides, unsystematic interviews on program’s strong and weak aspects were maintained. The results indicated Socrative program as a feedback agent could be benefited in learning process thanks to its accessibility, immediateness, and continuous interaction. The results also revealed that participants of the study perceived the program positively and attended the course more motivated. The study also reflected that students as prospective teachers more eagerly participated in digitally supported than traditionally maintained instructional activities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".