Did digital learning orientation, e-learning implementation and creativity reinforce students' innovation culture?
Bibliographic record
Abstract
The purpose of this study is to analyze the relationship between digital orientation and student innovation culture, the relationship between student creativity and student innovation culture and the relationship between e-learning and student innovation culture. This study uses a specific quantitative study by investigating the relationship between variables. These variables are measured using a research instrument, namely a questionnaire, so that the data obtained are in the form of numbers that can be analyzed through statistical calculations. The quantitative research method is chosen in this study because it uses data in the form of numbers that are analyzed by statistical calculations, and aims to test the established hypothesis. This study collects primary data using a survey method, namely distributing questionnaires to respondents in the form of Google Forms designed using a Likert scale of 1 to 5, which later the results of the questionnaire will be processed using the PLS-SEM application. Respondents in this study are 427 teachers who taught in elementary schools in Indonesia elementary school students, the sampling technique used is simple random sampling where each member of the population is given an equal opportunity to be selected as a sample. The results of this study are: Digital orientation has a positive effect on student innovation culture, Student creativity has a positive effect on student innovation culture and the influence of e-learning has a positive effect on student innovation culture. Digital learning is an educational innovation that can improve the quality of learning and support modern education. The benefits of e-learning include: Learning can be done anytime and anywhere, so it can reach more students, The teaching and learning process can be done more effectively and cost-effectively.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".