Videogame Education as an Anxiety Treatment between Middle-Year Students Post-Covid 19
Bibliographic record
Abstract
The present article seeks to provoke a discussion into how video games can be used in anxiety treatments and social stimulation tools among middle-year students and children of that same age. To do so, we initially start this article by reflecting on how the covid-19 pandemic disparate all anxiety alerts in our society including mental health issues such as depression and special anxiety disorders. Then we compare how the numbers of anxiety among children and young adults were already alarming before the lockdowns imposition and the social distance measures, especially in urban centers. This rising anxiety condition can be felt especially in the years that followed the social isolation of children especially because their social connection and recognition were just starting to grow and to establish important connections between their peers in-person and in virtual environments, throwing light on how to screen media and children`s homes are related with the anxiety increase and how we can investigate that phenomenon without succumbing to excessive positivism to today`s technology or to a deconstructive pessimism that leads us to distrust those media that are already in contact with middle-year students and children`s in that same age. To do so, we will resort to philosophical tools such as Edusemiotics and Cultural Studies to understand how games such as Minecraft and Roblox can be used in school environments to help students and teachers manage anxiety levels and surpass socialization issues past covid-19 lockdowns. To finish our reflection we also bring some data related to how those same media and games helped middle-year students to surpass social isolation and family disconnection during the pandemic while their kept exercising school content in those games, sharing and debating with their peers on virtual platforms.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".