Exploring the Relationship Among Preservice Teachers’ E-Learning Readiness, Learning Engagement, and Learning Performance in HyFlex Learning Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated the relationship among e-learning readiness, learning engagement, and learning performance of preservice teachers in HyFlex learning environments. To identify the causal relationship, data collected from 776 preservice teachers at four universities in the Philippines were analyzed using structural equation modeling (SEM). The results indicated that e-learning readiness and learning engagement are significantly related to students’ perceived learning performance. In addition, e-learning readiness mediates the relationship between learning engagement and learning performance. Given that the educational landscape has been transcending conventional delivery methods and now includes the HyFlex modality, education designers and learning facilitators must create dynamic and holistic learning delivery to enhance students’ e-learning readiness and learning engagement. Moreover, a student’s learning engagement may not be sufficient to predict the learning outcomes solely without the help of e-learning readiness in HyFlex learning environments. Findings shed light on which e-learning readiness construct is paramount for effective HyFlex learning environment design in education.
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.
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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.005 |
| 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 it