Growth and Goals Module: A Course-Integrated Open Education Resource to Help Students Increase their Learning Skills
Bibliographic record
Abstract
We developed and launched an online, course-integrated module called Growth & Goals aimed to help students better develop evidence-based learning skills. The module focuses on five main concepts: self-regulated learning, goal-setting, metacognition, mindfulness, and mindsets (growth and fixed continuum). Growth & Goals is an open education resource available for download at no cost to any educator through FlynnResearchGroup.com/GrowthGoals. The module is available in both French and English and can be customized to any university course. The module addresses the aforementioned concepts through a combination of text and videos, with interspersed interactive activities that students use to develop their learning skills. Growth & Goals is intended to help students effectively manage the challenges they may encounter as they progress through their postsecondary academic career and beyond and become more proficient learners. Since 2017, the module has been implemented in more than 15 university courses and has been used by over 8000 students. The preliminary evaluation of Growth & Goals has been largely positive, indicating that the module has been well received by both students and educators and that it successfully guides students in learning the module’s concepts.
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.
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 | 0.038 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".