If You Require It, Will They Learn from It? Student Perceptions of an Independent Research Project
Bibliographic record
Abstract
ALTHOUGH MOST TEACHERS BELIEVE that should write at least one in-depth paper during high school,1 the independent research paper is disappearing from high school curricula in the face of two competing pressures: the need to prepare for high-stakes tests and student senioritis. In 2002, William Fitzhugh of the Concord Review found that 62% of high school history teachers no longer assign papers of more than 3,000 words. Results from the 2006 High School Survey of Student Engagement revealed that 78% of high school seniors wrote no more than three papers longer than five pages in length; furthermore, nearly quarter wrote no papers of this length during their final year in high school. The lack of rigorous academic experiences in high school contributes to what Martha McCarthy and George Kuh call a substantial gap2 and what Michael Kirst calls a disconnect3 between the senior year of high school and postsecondary education. Indeed, nationally, more than half of the students enrolling in college require remedial courses in many subjects, including English,4 and significant number of recent high school graduates report feeling under-prepared to meet the expectations of college or the workforce.5 In many high schools, the senior year has become a blow-off time,6 and too many students leave high school without knowledge of how to conduct research or write an in-depth analytical paper.
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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 | 0.062 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".