The Power of Interest for Motivation and Engagement
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
The Power of Interest for Motivation and Engagement combines research from educational psychology and neuroscience to explain the important role of student interest in academics and life success. Drawing on cases both in and out of the classroom, the authors examine how interest is developed and sustained by learners, and assessed and interpreted by teachers. This volume is written for people who would like to know more about the power of their interests and how they could develop them: students who want to be meaningfully engaged, educators and parents wondering about how to facilitate motivation, business people focusing on ways in which they could engage meaningfully their employees and associates, policy-makers whose recognition of the power of interest may lead to changes resulting in a new focus supporting interest development for schools, out of school activity, industry, and business, and researchers studying learning and motivation. It draws on research in cognitive, developmental, educational, and social psychology, as well as in the learning sciences, and neuroscience to demonstrate that there is power for everyone in leveraging interest for motivation and engagement.
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 | 0.000 | 0.002 |
| 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 it