Coherence and Sense of Coherence: Conditions for Students’ Knowledge Development in Blended Learning Environments
Bibliographic record
Abstract
Constraints in the implementation of models of blended learning can be explained by several causes, but in this paper, it is illustrated that lack of sense of coherence is a major factor of these constraints along with the referential whole of the perceived learning environments. The question examined is how activating of models of blended learning in undergraduate education for teacher and radiograph affects the knowledge development. This is approached by mixed methods. The empirical data consist of data from surveys as well as focus group interviews and some observation studies. These data are analyzed and interpreted through a critical hermeneutical process of prefiguration, configuration and re-figuration. The findings illustrate significantly importance of sense of coherence among participants as a condition for implementing new designs and new learning environments. It is revealed that sense of coherence is both related to conditional matters as learning environments, structure, clarity and linkage but also preconditioned matters and prerequisites among participants related to experiences and convenience. It is stressed that this calls for continuous assessment and reflections upon these terms and conditions if the student shall be able acquire the necessary competencies.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".