E-Learning for Elementary Students: The Web 2.0 Tool Google Drive as Teaching and Learning Practice
Bibliographic record
Abstract
It is a well-known fact that during recent years, the new economic and technological environment, which hasemerged from the dynamic impacts of globalization, has given rise to the increased development of information andcommunication technologies that have immensely influenced education and training all over Europe. Within thisframework, there is an ever growing need for educators worldwide to effectively prepare their students for theassociated technological and social challenges; something that in reality will constitute the springboard towardsachieving future employment and social welfare. It thus becomes evident that technology in the new pedagogies ispervasive and is used as a means for discovering and implementing the deeper learning goals, namely those relatingto the creation of new knowledge in the world. In relation to the above, the present study focuses on the impact of theNew Pedagogies as perceived through the 21st century skills prism which are highly relevant to students’ futureacademic progress so as for them to achieve, on a long-term basis, personal and professional excellence. Specifically,in the context of creating new knowledge by using the power of digital tools, a case study which explores theexploitation of the web 2.0 Google Drive tool in 6th graders’ classes (elementary school) within Project-basedlearning (PBL) framework is examined. The research is expected to prove that the involved German educator isgoing to assist her students via a promising PBL activity so as for them to realize the value of ICT in the learning process.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".