The Transition from the Semester to the Quarter System
Bibliographic record
Abstract
This study examined how different academic calendars (semester vs. quarter) impact the experiences of students who transferred from a semester-based collegiate institution to the University of California, Riverside (UCR)'s quarter system.This population was compared with freshman enrolled students that transferred from high school and into their first year at UCR, excluding the optional summer sessions.About half of the participants were recruited from PSYC001 and PSYC002 via SONA (UCR Psychology Subject Pool) and were granted one research credit.Transfer students were mostly recruited through UCR's Transfers F1rst Program and some through SONA as well.Participants were 18 years or older, and they were provided a link to Qualtrics (a survey tool) which measured participants' study time, academic motivation, and stress levels based on their transition.Results indicated that freshmen students had higher levels of stress than transfer students, yet no significant differences were found for motivation and study time.Results also indicated that freshman enrolled students who reported higher levels of study time experienced higher levels of stress and motivation and stress and motivation positively correlated with each other.In contrast, transfer students who reported higher levels of study time experienced higher levels of stress, but stress and study time were unrelated to motivation.These findings suggest that education experiences differentiate across students' social environment and this may shed light for preparing these populations for the challenges of higher education with respect to the academic calendars.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".