Bibliographic record
Abstract
The overall topic for this research manuscript aims to understand how to best incorporate the use of digital technology into inquiry projects with the goal to create more authentic learning experiences for students. Moreover, what types of digital technology are available in order to support and enhance student learning and understanding and how can we best prepare teachers in order for them to feel comfortable using these forms of technology in the classroom. In order to answer this complex question, I surveyed my fellow teacher candidates using Google forms in order to better understand their experiences with digital technology and inquiry based learning. I also looked to two practicing teachers in very different schools and was curious as to what their experiences have been with the success of digital technology and inquiry. This research is very valuable because the amount of digital technology that is rapidly increasing and the paradigm of education is also shifting too. Learning is becoming much more learner centered than teacher centered as a result of inquiry based learning. Therefore, pairing the these two together, I believe can provide a much more rich, engaging and authentic learning experience if done properly. However, my research did indicate inquiry based learning is still a fairly new approach and teachers and pre-service teachers are learning themselves how to implicate this practice with their own teaching philosophy and practice. I also found that digital technology can enhance can significantly student learning and growth yet it can also act as a divider among students and different schools.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".