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Record W3106956710 · doi:10.5539/hes.v11n1p8

Simulation-Based Training: From a Traditional Course to Remote Learning - the COVID-19 Effect

2020· article· en· W3106956710 on OpenAlexvenueno aff
Albachiara Boffelli, Matteo Giacomo Maria Kalchschmidt, Avraham Shtub

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersUniversità degli studi di Bergamo
KeywordsCoronavirus disease 2019 (COVID-19)Context (archaeology)CurriculumCourse (navigation)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicMathematics educationMedical education2019-20 coronavirus outbreakComputer scienceTeaching methodPsychologyPedagogyMedicineEngineering

Abstract

fetched live from OpenAlex

The University of Bergamo switched from regular classes to online classes due to the COVID-19 pandemic during March 2020, without leaving to the students the chance to meet their teachers in the traditional setting even once. As such, this context represents a unique opportunity to compare the traditional courses, held in the years before, with remote learning. In this paper, we focus on the lessons learned from switching a project management course that combines traditional lectures with Simulation-Based Training (SBT) to an online course with the same structure, same curriculum and the same teaching team. Lessons learned are based on the opportunity to compare the two methods of teaching and their learning outcomes. Based on the analysis, conclusions about the future of this course and similar courses are presented.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.200
GPT teacher head0.451
Teacher spread0.251 · 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 designNot applicable
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

Citations6
Published2020
Admission routes1
Has abstractyes

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