Including Passion within Teacher-Candidate Assignments: How Genius Hour has created a more positive perspective on teaching and learning.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the educational world becomes more technologically inclusive, the need for teacher candidates to become proficient at integrating technology into their practice is crucial. Teacher Education programming in Ontario needs to reflect the current climate of K-12 teaching. In order to improve the learning environments for our teaching candidates, Teaching and Learning with Technology instructors decided to incorporate the concept of Genius Hour within our courses. Using this strategy, we hoped the teacher candidates would become more passionate within their learning, while developing the necessary technological, pedagogical, and content knowledge and skills. This study sought to understand the ways in which Teacher Candidate participation in Genius Hour influences their perceived participation within the course, as well as their opinions on the benefits of teaching with Genius Hour. According to teacher candidates, Genius Hour allowed for the time to focus on something of personal interest, with 2/3 of the participants seeing personal improvements in creativity and participation in their overall program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 it