Bibliographic record
Abstract
Blended learning is a buzzword in education used to describe any combination of face-to-face and online teaching methods. However, little information exists on teacher perceptions of blended learning and its application to education. This paper describes the results of a narrative inquiry into high school teachers’ use of blended learning and their perceptions of the benefits and challenges of blended learning. The narrative inquiry aimed to generate discussion on how best to use blended learning, knowing that today’s students are the first generation to have spent their entire lives surrounded by and using digital technology. It was conducted in a mid-sized high school located in a rural school division in a prairie province. The research method was qualitative, and the data collection included a questionnaire, interviews, and focus group interviews from a total of nine high school teachers. I also recorded reflections in a research journal. The research shows that benefits of blended learning include facilitating a shift from a teacher-centered to a student-centered classroom, making learning more convenient and flexible, increased student engagement and motivation, and creating a paper-reduced environment. Teachers expressed concerns related to the amount of time to redesign traditional face-to-face classes, the reaction of students, the risk of technical glitches, and limitations of the blended learning platform. Their responses also indicated that their beliefs about teaching and learning influence how they use blended learning. The paper concludes by discussing the study’s findings for practice and future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.190 | 0.108 |
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".