Blending Crowdvoting in Modern e-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
Given that the most students spend considerable time on social networks, many educational institutions use this habit as a basis for educational purposes. Increasing students’ active participation in learning activities is one of the main goals of education. The purpose of this research was to investigate to what extent crowdvoting techniques can increase students’ participation and interest in the e-learning process. Additionally, we set out to explore social networks as a medium for crowdvoting, contests, and collaboration among students. The research participants included 131 students in the information technologies area of the Faculty of Organizational Sciences, University of Belgrade who participated in contest related to their 3D modeling projects. Voting was performed via Facebook. The students voted for particular projects primarily based on the quality of the project itself. Additionally, the competition was an incentive for students to prove themselves to colleagues, but also to provide an opportunity for teamwork, additional engagement, and acquisition of new skills and knowledge. The research results indicate a generally positive attitude among students towards the competition and rewards.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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