Active Learning Using Boppps and Bloom’s Digital Taxonomy for Synchronous Online Classrooms
Bibliographic record
Abstract
E-learning has grown significantly over the past 20 years, and more instruction in higher education is occurring either wholly online or in hybrid modes. However, implementing active learning through these modalities can be complicated by the dual realities of instructors’ reticence and insecurities, and students’ anxiety about the experience. Furthermore, learning and navigating digital technologies can be challenging for both teachers and students. Drawing on my own experiences with instructing students within live online classes, this chapter outlines how standard BOPPPS lesson plans can be tailored via digital activities to promote active learning that meets all the skill levels on the Bloom’s digital taxonomy. In so doing, this chapter furnishes Bloom’s digital taxonomy with empirical strategies for achieving both higher and lower order learning objectives, and it contributes to educators’ efficacies with and capacities to design active online courses that enhance students’ learning experiences.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".