Promoting Success and Persistence in Pandemic Times: An Experience With First-Year Students
Bibliographic record
Abstract
The transition and adaptation of students to higher education (HE) involve a wide range of challenges that justify some institutional practices promoting skills that enable students to increase their autonomy and to face the difficulties experienced. The requirements for this adaptation were particularly aggravated by the containment and sanitary conditions associated with coronavirus disease 2019 (COVID-19). With the aim of promoting academic success and preventing dropout in the first year, a support program was implemented for students enrolled in two courses in the area of education at a public university in northern Portugal during the first semester of 2020/2021. Three sessions of 50/60 min were implemented, namely, the first session focused on the verbalization of the demands, challenges, and difficulties of the transition, and the second and third sessions focused on the difficulties of academic adaptation and academic performance. Data from a dropout risk screening instrument and from the activities performed during sessions were analyzed. The main results point to student satisfaction with the content and the activities of the sessions and their usefulness. Students report not only high satisfaction levels with HE attendance, but also some emotional exhaustion due to academic activities. The continuity of the program is recommended with some improvements in its planning to ensure a more definitive version of the program in the next two years.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".