Simulating “Normalcy” in a Global Pandemic: Synchronous e-Learning and the Ethics of Care in Teaching
Bibliographic record
Abstract
method also was used to replace in-class discussions by giving students the opportunity to participate virtually.The discussion forum comprised 15% of their overall grade.I maintained a tally of weekly points for students who used the forum.The results were somewhat mixed.Most students who had been fairly engaged continued to post to the forum; others apparently ignored it.As online delivery continues into the fall, use of the discussion forum will be tweaked to make it mandatory for all students to submit at least one response every week-even if that response is a brief reflection or comment (Christopher, Thomas, and Tallent-Runnels 2003).Fourth, I gave students the option to upload their papers through Turnitin or email.I preferred receiving papers as Word documents via email so I could use the "track changes" function to edit and grade.This was an easier option because I find some Turnitin editing and commenting features to be unwieldy.The final exam-a combination of three-to five-sentence conceptual definitions and five 150-word short essay questions-was altered to a take-home-exam format.Students were provided a template with the final-exam questions two weeks in advance.Papers were to be returned on an assigned due date during the scheduled finalexam period.Most students were diligent and found it quite easy to follow these instructions.Although I returned student papers within a week, grading online was significantly labor intensive (Lao and Gonzales 2005; Sellani and Harrington 2002).At the end of the course, I used a Feedback tool on Moodle to compile my own evaluations that asked students basic questions on course content in addition to the main tools they preferred in remote learning.Most students were pleased to have the option of both synchronous meetings and recorded lectures.Many reported that they were satisfied with my communication and that I had maintained the same momentum as an in-person class by keeping the content, structure, and objectives the same.Although I managed to use online tools in the spring and will continue using them in the fall, teaching daily classes on Zoom can be exhaustingespecially with a higher teaching load.There is something deeply limiting about not having the physical and mental space to move around in the classroom.Interpersonal interactions also are more difficult, making student participation challenging.Mutual respect and setting ground rules are important principles for me, especially as a female instructor.Student privacy is an important concern; however, if synchronous lectures become the norm in the fall, then-in this spirit of mutual respect-I will expect my students to be present at all online meetings for my courses.Key to managing online instruction without too many obstacles-at least in my case-were flexibility and communication (Jones, Kolloff, and Kolloff 2008).I cannot emphasize this enough.Given the unnatural circumstances in which both faculty and students find themselves, being flexible and clearly communicating ideas are crucial.This means relaxing unreasonable expectations of students.Successful online instruction depends on delivering course content as simply as possible without complicating or adding to/revising the curriculum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".