What Matters to Student Success : Lessons from High Performing Colleges
Bibliographic record
Abstract
We all want the same thing We all want the same thing -an an undergraduate experience that undergraduate experience that results in high levels of learning results in high levels of learning and personal development for all and personal development for all students.students. Overview OverviewWhat the world needs now Why engagement matters Lessons from highperforming institutions Implications Advance Organizers Advance Organizers To what extent do your students To what extent do your students engage in productive learning engage in productive learning activities, inside activities, inside and and outside the outside the classroom?classroom?How do you know?How do you know?What could we do differently What could we do differently ---or or better better ---to enhance student to enhance student success?success?Student Success in College Student Success in College Academic achievement, engagement Academic achievement, engagement in educationally purposeful activities, in educationally purposeful activities, satisfaction, acquisition of desired satisfaction, acquisition of desired knowledge, skills and competencies, knowledge, skills and competencies, persistence, attainment of persistence, attainment of educational objectives, and post educational objectives, and post -Most Important Skills Employers Look For In New Hires Teamwork skills Critical thinking/ reasoning Oral/written communication Ability to assemble/ organize information Innovative/thinking creatively Able to work with numbers/statistics Foreign language proficiency
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".