Growth and Goals: Independent
Bibliographic record
Abstract
This module can be used by anyone in any context and is intended to help you become a more proficient learner, whether in academic, physical, artistic, or other contexts. As an open education resource, it can also be adapted. The Growth & Goals Module is developed by members of the Flynn Research Group at the University of Ottawa. The Flynn Research group is primarily focused on Chemistry Education Research (CER) and works to develop innovative tools and methods to support students learning. The Growth & Goals module is a Self-Regulated Learning (SRL), Growth Mindset, and Metacognition Module for post-secondary learning and beyond. The module allows students to address their strengths and weaknesses, to identify their current mindset towards their goals and learning, and to develop the SRL skills necessary to take control of their learning. University students have to learn in many different formats, often confront failure, and manage many different courses and life expectations simultaneously. To be successful, students need to know and continually monitor their learning plus develop autonomy and professional capacity skills. The Growth & Goals module aims to help students with this and to develop the framework and skills necessary to manage their learning and be successful in a post-secondary setting. For more information, visit our website here.The Growth & Goals Module is developed by members of the Flynn Research Group at the University of Ottawa. The Flynn Research group is primarily focused on Chemistry Education Research (CER) and works to develop innovative tools and methods to support students learning. The Growth & Goals module is a Self-Regulated Learning (SRL), Growth Mindset, and Metacognition Module for post-secondary learning and beyond. The module allows students to address their strengths and weaknesses, to identify their current mindset towards their goals and learning, and to develop the SRL skills necessary to take control of their learning. University students have to learn in many different formats, often confront failure, and manage many different courses and life expectations simultaneously. To be successful, students need to know and continually monitor their learning plus develop autonomy and professional capacity skills. The Growth & Goals module aims to help students with this and to develop the framework and skills necessary to manage their learning and be successful in a post-secondary setting.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.152 | 0.093 |
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".