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Record W2900546272 · doi:10.1017/cts.2018.210

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

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

Bibliographic record

VenueJournal of Clinical and Translational Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsTranslational scienceTranslational researchCourseworkMassive open online coursePopulationMedical educationMedicinePsychologySociologyMathematics educationSocial science

Abstract

fetched live from 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.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.113
GPT teacher head0.478
Teacher spread0.365 · 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".

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Citations0
Published2018
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