Bibliographic record
Abstract
Online learning environments have been used intensively during the COVID-19 pandemic and are frequently preferred alternative learning environments afterward. On the other hand, the lack of adequate learning applications for online environments negatively affects teaching. The main purpose of this research is to develop problem-based learning (PBL) activities for online learning environments within the scope of Physics course and to reveal the application processes. The study was carried out with 97 students in the fall semester of the 2020–2021 academic year. PBL applications were carried out in online learning environments with the interaction of online groups of 5–7 people through the Zoom program. A qualitative research approach and critical action research model were used in this research. The data were obtained with the help of rubric form, interview, peer assessment, peer group assessment, and documents and evaluated with content analysis and descriptive analysis. In the process of PBL activities in online learning environments, students took an active role as a part of their learning processes, interacted constantly with their peers, and demonstrated high-level success in their learning competencies by fulfilling their responsibilities. In online learning environments, there is a need for application examples where all students can demonstrate their process skills and student-centered assessment–evaluation applications that will determine the application outputs.
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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 | 0.003 | 0.013 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".