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Record W4295941751 · doi:10.5430/ijhe.v11n5p169

Guidance as A Key Factor for Quality Outcomes in Experiential Learning and Its Influence on Undergraduate Management Students throughout the Covid-19 Pandemic

2022· article· en· W4295941751 on OpenAlexvenueno aff
Anat Shteigman, Michal Levi-Blech, Arie Reshef

Bibliographic record

VenueInternational Journal of Higher Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsInternshipExperiential learningFlexibility (engineering)Quality (philosophy)PsychologyMedical educationCoronavirus disease 2019 (COVID-19)PopulationKnowledge managementMedicinePedagogyComputer scienceManagementEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.519
Teacher spread0.438 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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