THE INFLUENCE OF PEER EDUCATORS IN A FIRST-YEAR SEMINAR ON FRESHMAN PREPARATION FOR COLLEGIATE CHALLENGES: A QUALITATIVE INVESTIGATION OF OBSERVATIONAL LEARNING
Bibliographic record
Abstract
Most high school students have not spent deliberate time preparing for their transition to college. Knowing this, institutions have developed a first-year seminar geared toward transitional issues inherent to a specific institution. While the research on these programs illustrates their utility, there appears to be an opportunity to further their success by incorporating peers as educators in the classroom. Bandura (1986) saw the potential of observational learning through peer modeling, though few researchers have studied first-year seminars from this theoretical perspective. Through a postpositivistic philosophical paradigm, this exploratory qualitative study utilized a phenomenological design to investigate two research questions: what are the academic and social challenges freshmen face in the transition to a small, private, highly selective, STEM-focused institution and how does the presence of sophomore peer educators in a first-year seminar influence freshman preparation for those fall quarter challenges. A total of 41 freshmen participated in the study. Data were collected through student journals and focus group interviews. The results of this study confirm that the transition to this specific type of institution is just as complex as the transition to other types of institutions, with students reporting similar academic and social challenges as found in the literature. However, their emphasis was on the core (i.e., academic) rather than the periphery (i.e., social) of the collegiate experience. The application of modeling, however, was not strong enough to determine whether observational learning influenced these transitional challenges.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".