Design Considerations for Supporting the Reluctant Adoption of Blended Learning
Bibliographic record
Abstract
Blended learning, described as the integration of online and classroom teaching, can range in complexity from the augmentation of traditional instructional methods to transformational course redesign. In this case study, an introductory communication course was redesigned by a team to blend online and classroom learning. Where team design approaches typically involve instructor participation, thereby allowing them to reconceptualize their teaching using technology, this paper examines how various design choices, with little instructor input, affected their ability and willingness to adapt to the blended course environment. Through an analysis of semi-structured interviews with instructors over two iterations of the course design, this paper provides insight into how the instructors, who were experienced with teaching in a traditional setting, struggled with the new format. The analysis reveals three themes connected to pedagogical decision-making–consistency versus flexibility, pedagogical dissonance, and student–instructor engagement–that describe the challenges instructors face in this changing environment. Suggestions are offered for institutions and course designers looking to implement blended learning programs.
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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 | 0.060 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".