The Influence of Learning Communities on the Interaction Levels of Developmental English Students
Bibliographic record
Abstract
Achievement and retention of students are significant concerns for American community colleges. While 86 percent of the students surveyed by the Community College Survey of Student Engagement (CCSSE) (McClenney, 2004) indicated a goal of completing a certificate or associate’s degree program, less than a quarter of those enrolled in the 1995-1996 academic year earned this credential in the subsequent six-year period. For students entering college underprepared, these risks are magnified, increasing the possibility of low satisfaction rates, low achievement rates, and high attrition rates. Demographically, underprepared students are similar to the overall population of community college students. However, research shows that they often have a more difficult time connecting with the academic environment, are uncertain of their goals, have little academic direction, and share many of the non-cognitive characteristics found in first-generation and minority students (McCabe, 2003). These risks are important, because as Stephens (2001) found, 41 percent of freshmen at two-year colleges are enrolled in developmental courses. Studies on retention that have been applied to developmental students have concentrated on the high attrition rates, but few have considered the theories of Astin or Tinto in relation to these students. Even fewer have reviewed the influence of learning communities on developmental English students.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".