The contribution of the Internet into learning
Bibliographic record
Abstract
Nowadays, the expansion of the Internet is, undoubtedly, widespread and has developed a new socio-economic environment, where information, innovation and knowledge play a primary role. Through its multiplicity the Internet constitutes probably the best way for accessing entertainment, learning and information, as well as for establishing socialization processes and communication among people.This paper examines issues related to the learning process, the learning environments developed by the new virtual reality and the relationship between learning and the Internet, with a particular focus on the impact of the Internet on informal learning processes. The survey mainly aims at investigating university students’ beliefs about the impact of the Internet on the learning process. The sample is comprised by 390 students from various Greek university departments, 160 (41%) males and 230 (59%) females. The majority of the students believe that the Internet can significantly contribute into the learning process. More specifically, they state that the Internet use can improve learners’ academic performance, promote research skills and critical thinking, encourage independent or collaborative learning, enhance motivation, strengthen self-confidence and improve the teaching methods. It facilitates the access to information that the educational system fails to provide, and offers knowledge, frequently more useful than that provided by the courses, complementing, thus, “formal” learning. The research findings also show a differentiation in Internet use, which is associated with the educational level of students’ parents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".