Effectiveness of Online Learning with the Web-based TPACK Scaffolding for Enhancement TPACK Ability of Pre-service Chemistry Teachers
Bibliographic record
Abstract
This study investigates the effectiveness of web-based TPACK scaffolding to enhance the TPACK ability of pre-service chemistry teachers through online learning. Participants in this study were 74 pre-service chemistry teachers in Chemistry Education program who took a chemistry learning planning course. This study used the quantitative research method approach. The experimental study with pre-and post-test design examined the more significant increase in TPACK ability between the experimental and control classes. The research instrument consisted of 20 multiple-choice questions containing the TPACK components. Analysis of pre-and post-test data used the stacking and racking method in the Rasch model. The stacking analysis result indicated that the pre-service chemistry teachers' ability increased from pre-test to post-test. The racking analysis result indicated that the pre-service chemistry teachers could answer TPACK items easier in the post-test conditions after being given intervention. The various types of scaffolding available in web-based TPACK online learning effectively support pre-service chemistry teacher TPACK ability enhancement. Online learning with web-based TPACK scaffolding is advisable to develop pre-service chemistry teachers' TPACK to prepare them better to use various types of technology in classroom learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".