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Enregistrement W2900546272 · doi:10.1017/cts.2018.210

2358 Expanding our educational reach: Development of a massive open online course (MOOC)

2018· article· en· W2900546272 sur OpenAlexaboutno aff
Nicole L. O’Dell, Eric Fredericksen, Sarah E. Peyré

Notice bibliographique

RevueJournal of Clinical and Translational Science · 2018
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetics, Bioinformatics, and Biomedical Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTranslational scienceTranslational researchCourseworkMassive open online coursePopulationMedical educationMedicinePsychologySociologyMathematics educationSocial science

Résumé

récupéré en direct d'OpenAlex

OBJECTIVES/SPECIFIC AIMS: Translational Science 101 aims to: (1) Orient the public to the field of clinical and translational science; (2) Provide a brief overview of each phase of translation (T0-T4); (3) Provide real-world examples of clinical and translational researchers and research projects that have directly impacted patients; (4) Provide learners with information on how they can become involved in clinical and translational science through many different avenues (study volunteer, student, faculty member, or study coordinator). METHODS/STUDY POPULATION: The primary audience for Translational Science 101 is the general public and media outlets who are interested in learning more about clinical and translational science and how this research is improving population health. The University of Rochester Clinical and Translational Science Institute created the course in order inform the public about the field of clinical and translational science, orient the public to the types of research that fall under the translational science umbrella, and demonstrate how translational research impacts populations. The Coursera Massive Open Online Course (MOOC) platform was selected to host the course in order promote the greatest level of exposure and also to expand the educational reach of the UR-CTSI to new external audiences. The course was constructed from scratch utilizing the Community of Inquiry (CoI) framework, an approach that is often utilized to guide the design and construction of asynchronous online coursework. CoI highlights the elements of social presence, cognitive presence and teaching presence as key factors impacting the educational experience learners have when enrolled in an online course. Discussion boards, embedded quizzes, and end of module quizzes were integrated in to the course design to promote learner engagement, collaborative learning, and interactions among learners. The “storytelling” instructional strategy is the backbone of the Introduction to Clinical Science modules, with various researchers from the University of Rochester Medical Center explaining their lines of research and how the research impacts patients and communities. Educational research has shown that there are many benefits to including storytelling in instruction (Green, 2004; Geanellos, 1996), including: (1) Stories create interest: The narrative structure increases learner interest and engagement as they are drawn in to a good story. (2) Stories create a more personal link between the learner and the content: Storytelling allows exploration of shared lived experiences without the demands of practice and allows students to make connections between the shared experiences and their own previous experiences and knowledge. (3) Stories provide a structure for remembering course materials: The inclusion of stories facilitates remembering because it is easier to remember a story rather than a list of disparate facts, and stories evoke vivid mental images which are an excellent cue for recall. (4) Stories are a familiar and accessible form of sharing information: Storytelling aids in overall learner understanding as it is a nonthreatening way of sharing information. Storytelling can also enhance course discussions as students feel more at ease discussing a story than discussing abstract or new concepts that they are still in the process of mastering. RESULTS/ANTICIPATED RESULTS: Introduction to Translational Science was launched on October 16, 2017, and is automatically scheduled to begin a new session every 3 weeks. To date the course has reported the following analytics: (1) 2308 learners have visited the course page, (a)476 learners have enrolled in the course; (b) 244 learners are currently active in the course; (c) 11 learners have completed all of the requirements of the course. (2)Learners by Continent, (a) North America 31%; (b) Asia 30%; (c) Europe 23%; (d) Africa 9%;(e) South America 5%; (f) Oceania 2%. (2) Learners by Country: Learners have come from 84 different countries from around the world. The 15 highest enrollment numbers are: (a) USA 25%, (b) India 11%, (c) Egypt 3.7%, (d) United Kingdom 3.4%, (e) Mexico 3.2%, (f) Brazil 2.8%, (g) China 2.8%, (h) Saudi Arabia 2.2%, (i) Spain 2.2%, (j) Germany 1.7%, (k) Russian Federation 1.7%, (l) Malaysia 1.5%, (m) Turkey 1.5%, (n) Italy 1.5%, and (o) Canada 1.5%. (3) Gender: 48% women and 50% men. (4) Age: (a) 13–17: 0.72%, (b) 18–24: 19.6%, (c) 25–34: 44%, (d) 35–44: 14.4%, (e) 45–54: 8.6%, (f) 55–64: 7.2%, (g) 65+: 3.6%. (5)Highest Education Level o Doctorate Degree: 17%; (a) Professional School Degree: 14%; (b) Master’s Degree: 31%; (c) Bachelor’s Degree: 27%; (d) Associate’s Degree: 2.3%; (e) Some College But No Degree: 4.5%; (f) High School Diploma: 3.8%; (g) Some High School: 0.75%. DISCUSSION/SIGNIFICANCE OF IMPACT: The Massive Open Online Course (MOOC) platform offers new, exciting opportunities for CTSA institutions to create courses and trainings that are accessible by learners all over the world. This greatly expands the educational reach that the CTSA education programs can have, moving beyond hub-focused or consortium-focused education to a much broader audience. The expansion of educational reach can promote increased visibility of the CTSA program, encourage collaborations amongst researchers at different institutions, and also inform the public about clinical and translational science, potentially fostering advancement opportunities.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,722
Score d'incertitude au seuil0,379

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,113
Tête enseignante GPT0,478
Écart entre enseignants0,365 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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