Learning Styles and the Hybrid Learning: An Empirical Study about the Impact of Learning Styles on the Perception of the Hybrid Learning
Bibliographic record
Abstract
Abstract Learning style is a crucial element in the academic life of students. It plays a fundamental role in the selection of a suitable teaching method of instruction. The aim of this research paper is the study of the perception of the hybrid learning and students’ learning style. In addition, another objective of the study is to analyze the perception of the hybrid learning in accordance with the learning style. This paper employs quantitative research and the descriptive and comparative methods. The study sample consists of 89 Albanian university students. Data analysis was conducted through SPSS 20 and JASP-0.8.1.2. The statistical analyses utilized in this paper are distribution tables, crossed tabulation, student test, Pearson correlation coefficient, Bayesian Independent Samples T-Test, and One - Way ANOVA. The conclusion of the data analysis shows that most students used the visual learning style in order to study. Students have a positive perception of the hybrid learning. There exists an insignificant statistical correlation between learning styles and the perception of the hybrid learning. The area of study has an impact on the components of the hybrid learning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".