Back

Running System of Flipped Teaching Based on Video Conference

Zhang, X.-Y.

2022-10-13 scientific communication and education
10.1101/2022.10.10.511270 bioRxiv
Show abstract

ObjectiveI aimed to provide a basis for the standardized operation of Flipped Teaching based on Video Conference for reference by other institutions or organizations. MethodsThe teaching and research materials of Flipped Teaching with Video Conference as carrier carried out in April and June 2022 were collected. Based on the "Structure-Process-Outcome" theory, the induction method and SWOT analysis method were used for systematic analysis. ResultsA total of 43 residents participated in the teaching project, and 40 of them passed the examination, accounting for 93.0%. And the teaching project had been analyzed and reported in 3 literature. For the successful operation of this project, the following five dimensions were summarized. First, the preparatory dimension of teaching: clear teaching form, objectives, content, reference materials and assignments; Second, the carrier of teaching implementation: the selection, function and debugging of teaching equipment; Third, the teaching management dimension: the management mode, management team, lecturers team, trainees management and emergency response; Fourth, the teaching evaluation dimension: trainees systematic evaluation of the teaching; Fifth, the teaching assessment dimension: attendance rate, assignments completion rate and exam pass rate. ConclusionThe core of the running system is to carry out Flipped Teaching with Video Conference as carrier to realize the standardized training for residents, and ensure the smooth progress and the quality of teaching through teaching management, evaluation and assessment.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.