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Enregistrement W3188479253 · doi:10.18260/1-2--36984

Discovery: Transition of an Inquiry-focused Learning Program to a Virtual Platform During the COVID-19 Pandemic (Evaluation)

2024· article· en· W3188479253 sur OpenAlexafffund
Nicolas Ivanov, Nhien Tran-Nguyen, Neal I. Callaghan, Theresa Frost, Jose L. Cadavid, Huntley H. Chang, Ileana L. Co, Patrick Diep, Guijin Li, Nancy Li, Corinna Smith, Joshua Yazbeck, Locke Davenport Huyer, Dawn M. Kilkenny

Notice bibliographique

Revue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Langueen
DomaineComputer Science
ThématiqueOnline Learning and Analytics
Établissements canadiensUniversity of Toronto
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoAmerican Society for Engineering Education
Mots-clésPandemicCoronavirus disease 2019 (COVID-19)Transition (genetics)Computer scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakData scienceVirologyMedicineChemistry

Résumé

récupéré en direct d'OpenAlex

Abstract The shift to distance learning in response to the COVID-19 pandemic has presented teachers and students with several challenges. Teachers have found themselves quickly creating distance learning materials to provide equal or greater educational opportunity and engagement as in-person instruction. This shift is met with parallel increased demand on students to independently manage their learning and coursework with the absence of in-person supervision, support, and peer interaction. In this work, we describe our approach and observations in transitioning Discovery, a secondary student science, technology, engineering, and mathematics (STEM) education program, to a virtual platform. Developed by graduate students in 2016, Discovery was designed to engage secondary students in semester-long inquiry-based projects within the context of biomedical engineering. Projects are designed to foster and reinforce critical thinking skills required for post-secondary study. Throughout the semester, students design and execute experiments within post-secondary laboratories with instructional support from both their teachers and graduate student volunteers. In response to university teaching space closures in early 2020, we developed and delivered a virtual offering of Discovery. In contrast to in-person delivery, this initial virtual offering placed greater emphasis upon quantitative analysis rather than experimental design and execution. Access to virtual laboratory simulations was provided as a substitute for in-laboratory skill development. While overall assessment of student (survey instrument) and teacher (interviews) experiences revealed a highly positive perception of the program experience, areas for improvement were also highlighted. Many students reported struggling with motivation to keep up with course materials and soft deadlines (60%) as well as the lack of guidance provided by in-person mentor and teacher interactions (50%). Teacher interviews echoed quantified student perceptions, but further identified lamentation at the loss of student-driven, open-ended, and iterative problem-solving opportunities typically afforded by Discovery. Consequently, we developed an adjusted virtual program for the Fall 2020 term. The redesigned program reintroduced the open-ended aspect of previous in-person projects, and rather than including access to commercially available virtual laboratory simulation, greater focus was placed on design of experimental procedures that were evaluated and simulated by graduate students. Additionally, greater care was taken to discretize project components and deliverable deadlines to provide enhanced structure and guidance for students. We observed this updated program structure to similar outcomes of in-person offerings. A slight majority (51.4%) of Fall 2020 students achieved higher grades for Discovery deliverables than other class assessments. In post-program surveys, ~49% of students indicated they are more likely to pursue STEM courses, ~89% would participate in the program again, and ~78% responded that the experience made them more comfortable with completing university or college level laboratory work. While these results were encouraging, comparisons to previous in-person outcomes and analysis of teacher experiences (interviews) highlighted persistent gaps in student experience while completing the program virtually.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,911
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0030,006
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,123
Tête enseignante GPT0,371
Écart entre enseignants0,248 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission2
Résumé présentoui

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