Guidance as A Key Factor for Quality Outcomes in Experiential Learning and Its Influence on Undergraduate Management Students throughout the Covid-19 Pandemic
Bibliographic record
Abstract
This paper presents a study that explores how “field experience” programs generate a meaningful bridge between the “theoretical” academic world and the “real” labor market. We examine this model in a population of undergraduate management students who participated in experiential learning programs via internship-integrating courses. The results unravel the significance of the experience-based educational program during Covid-19, formulating new correlations unique to this period. The study's contribution focuses on three main areas. First, the findings shed light on the academic supervisor's importance in establishing the quality of the program and consequently improving students’ perception of its contribution to their integration in the employment market. Moreover, we found that the contribution of the guidance provided by the organizational mentor diminished during the Covid-19 period compared to that shown in former studies. Additionally, an innovative mediating effect of the guidance provided by the organizational mentor was found, one that generated an association between the quality of the program and its contribution to integration in the employment market. These results receive further validation during the period of the study, when academic institutions were required to show flexibility and adaptation, leading to the utilization of previously uncustomary distance learning methods.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".