Implementing a Virtual Learning Environment to Enhance Modeling skills and Collaboration in a Pre-service Teacher Education Science Program: Students' Ideas about a Blended e-Learning Approach.
Bibliographic record
Abstract
This paper reports on a research study targeted at a) the development of modeling skills of pre-service teachers, through the use of Stagecast Creator and b) the enhancement of collaboration among students, through the use of WebCT. Participants were 20 pre-service teachers who attended a science course at the University of Cyprus (spring semester 2004). A blended e-learning approach was used, as the course incorporated e-learning through the use of WebCT combined with face-to-face teaching. The study took advantage of platform functions for presentation of information, synchronous and asynchronous communication and students' access to models developed by other students for assessing and refining them. Means of data collection include students' reflective journals, reports of the synchronous discussions, and a questionnaire. Data analysis indicates that students believe that the blended e-learning approach critically enhanced the development of modeling skills and the collaboration among them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".