Blended Learning Effectiveness and College Students’ Deep Learning Perceptions: The Community of Inquiry Perspective
Bibliographic record
Abstract
Emerging technologies and innovative instructional methods have revolutionized education, making blended learning the new standard in the artificial intelligence era. However, poor integration of online and face-to-face learning has led to challenges such as superficial student engagement. This study developed a Community of Inquiry-based blended learning model and evaluated its effectiveness with 92 college students using a quasi-experimental approach. Over 16 weeks, the experimental group (n = 48) adopted the blended learning model, while the control group (n = 44) used traditional learning conditions. Learning effectiveness and deep learning perceptions were evaluated, revealing the blended learning group demonstrated superior learning effectiveness (d = 0.83) and reported higher deep learning perceptions (η2 = .05–.072) compared to the traditional learning group. These results provide valuable insights for educators aiming to design blended learning models that foster deep learning and improve overall learning effectiveness.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".