Computer Aided Training - A Variable of the Modern Romanian Educational Process in the Knowledge Society
Bibliographic record
Abstract
Computer and multimedia learning is still an active teaching method. Computer-assisted and computer-based training allows an education based on the intellectual profile of the student and beyond. It also puts the student in situations of interaction and rapid communication, made in a favorable environment that allows massive dissemination of the created content and time flexibility by combining synchronous and asynchronous means of communication. In the case of computer-assisted instruction, the interactivity is practically generalized, providing the learner with permanent feedback, as visible and immediate effects occur on the computer screen. Today, computer-assisted instruction involves efficient research of the student's work, supervised and guided by the teacher (even in the current conditions dictated by the pandemic), which helps him in performing technical operations, in identifying the links between information and documentation, leading him to a new form of knowledge (digital knowledge). Achieving the objectives of the educational process requires as necessary, in different stages of learning, the intuition of processes and phenomena of reality, either directly or through substitutes. At the same time, the formation of skills and abilities requires the presence of material supports for practicing actions. Through this paper, we aim to show that there is thus a diversity in education such as "pedagogical tools" called associated teaching aids in the field of computer-assisted training. They contribute to the efficient development of the didactic activity, being equally material resources of the educational process, selected from reality, modified, or made to reach the pedagogical objectives. The progress of technology has driven the diversification and improvement of these resources, so that, in the Information and Knowledge Society, it can be shown that computer-assisted training has become a variable of the modern educational process, which must take into account, more than ever, the profile of the individual and society.
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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 | 0.001 | 0.002 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".