Training and Instruction Skills Through the Test of Time
Bibliographic record
Abstract
This study involves e-learning skills via educational software, compared to instruction via educational software with the mediation of an instructor. In the last two decades, the role of the teacher-lecturer has changed, from teaching to guidance and instruction. The technological tools have changed the nature of the learning space and the manner in which the teacher interacts with his students. Educational software is a collection of digital pages, packaged as a learning unit, and is a common tool for delivering self-instruction in academia on a range of issues. This is despite the fact that the effectiveness of this tool in academia has not yet been tested. In addition, the educational software is a technological tool but it is not being updated regularly, therefore the development of the topics in educational software is low. The key motif of technological advancement is to enable constant updates, and therefore, the effectiveness of this learning tool, which has the potential of countering the need for the dynamics of content transfer with its static nature, must be examined. The current study aims to examine the use of this tool in teaching and instruction, and to examine the ways to bridge over this gap of a "static" tool and a "dynamic" learning world. The study focuses on a case study in the Israeli Air Force and integrates instruction with technologies means. We have looked into the skills of e-learning through educational software, as well as the contribution of the instructor to the teaching process. The study's literature reveals that e-learning focuses on the cognitive aspect of learning and on the knowledge of the instruction field. Yet there are studies that engage in reinforcing the in-person communication, meaning, the significance of a “face-to-face” encounter between the student and his instructor. We examined the probability and the extent of the added value of the teacher/instructor in e-learning through educational software. An examination of e-learning through educational software is conducted by a test that consists of questions broken down into levels according to the STEM Model. The findings of the study demonstrate the contribution of educational software as a means of instruction, when it is combined with an in-person encounters between the students and their instructor. We found that combining the in-person meetings with the educational software practice has vastly improved the motivation of the technicians in training, their learning experience and the learner’s ability to understand the learning material.The results of our study shed a spotlight on the instruction, which are a major part of the teaching process in general, as well as the use of educational software as a relevant and applicable mean in the training process in particular. The case study, conducted in the Israeli Air Force, which guides the training processes that are held in the army, is the first case study of its kind, which tracks the use of educational software as a means of instructional work. Our assumption is that training work using educational software has a high influence in the context of teaching and training in different and diverse institutions and organizations, such as in academia.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".