Blending Crowdvoting in Modern e-Learning Environments
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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".