How big data is used as a key element for hybrid university education
Bibliographic record
Abstract
Hybrid learning in universities is the blending and mixing of the learning environments, this includes both face-to-face (FTF) which implies classroom instruction and online environment (E-learning) as well. According to De Mauro, Greco and Grimaldi (2016), Ellis’ study shows that hybrid learning provides the students with the opportunity to understand and explore the real world at the same time through various authentic experiences. Authentic experience as cited by De Mauro, Greco and Grimaldi (2016) can be facilitated in the online learning environment through coming up with sufficient online learning or by blending learning to combine both online and FTF learning. The main objective of hybrid learning is to enhance effective and efficient experience through a more improved delivery model. This study is based on the review of previous articles using PRISMA methodology, it focuses on the big data as key element in hybrid learning in university education. The main objective of this study is to review 40 articles published in Scopus within 2010 to 2022 subject to big data in education, hybrid learning in universities or higher learning institutions and based on their findings the study come up with a conclusion as discussed below.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".