Exploration and Application of Three-stage Strengthening Mode based on Massive Open Online Course to Improve Students’ Competence in Electrocardiogram Interpretation Skills
Bibliographic record
Abstract
Electrocardiogram (ECG) is a widely common diagnostic test in clinical practice. However, research has shown that residents and medical students cannot analyze ECG skillfully. In this study, we explore a three-stage strengthening mode based on massive open online course (MOOC) to improve students’ competence in ECG interpretation skills. We carry out the first stage in a diagnostics course, in which instructors adopted online and offline blended mode for the teaching method of ECG with clinical medicine students in grade 2018 based on MOOC. We compare scores of ECG interpretation skills for 91 students in grade 2018 with 81 students in grade 2016 to evaluate the effect. The second stage concerns studying internal medicine, in which we divide students in grade 2018 into two groups with 49 students in group A and 42 students in group B. Group A strengthened their ECG interpretation skills when learning about heart diseases. The third stage pertains to rotation in the internal medicine department in the internship period. Some students in group A continued to receive intensive training. Students in group B had no reinforcement arrangement in the second and the third stage. Students in grade 2018 took graduation tests at the end of their internship. We use the scores on ECG interpretation skills in the graduation examination to evaluate the strengthening effect between group A and group B. The result of the first stage shows that scores on ECG interpretation skills in the final exam in grade 2018 were 19.32 ± 4.62 points, which is higher than those of grade 2016, whose points were 16.89 ± 5.30 (total scores in both grades were 30 points). Difference is statistically significant. Scores on the graduation examination in group A were 38.89 ± 16.81 points, and those of group B were 26.86 ± 15.43 points (total score was 60 points), with statistical difference between these two groups. The three-stage strengthening mode based on ECG MOOC is an effective method to improve students’ ECG interpretation skills. Results show that an online and offline blended teaching strategy is superior to the traditional lecture method. Repeated practice is necessary for improving ECG interpretation competence. Students in the strengthening program group had better grades in the graduation examination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".