The Effect of Assimilating Learning Management Systems on Parent Involvement in Education
Bibliographic record
Abstract
In recent years a new culture of online interactivity and pedagogic management through learning management systems has been gradually acquiring force, in addition to the traditional culture of face-to-face interactions as well as use of other media. Although much research work has been done in recent years on the significance of parent involvement in the educational process, no thorough research has been conducted on the effect of exposure to learning management systems that offer students and their parents maximal transparency of the educational environment, including the level of communication between the student’s parents and the teaching staff and their level of involvement in the educational process, particularly in the current period during the outbreak of the coronavirus crisis that has forced the educational system and other routine systems to switch to an online sphere against their will. The current work examined the effect of assimilating an online learning management system, the Mashov system (a Hebrew acronym for immediacy, transparency, and monitoring) on parent involvement in their children’s educational process. The significance of the research topic is particularly evident in these uncertain times, when the traditional learning environment is being compelled to make way for distance learning due to the outbreak of the coronavirus crisis, assisted by learning management systems and primarily the Mashov system. Therefore, there is room to enhance research in this field throughout the crisis and, once it is over, in further valuable studies.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".