Bibliographic record
Abstract
A serious game is a computer application that combines a serious intention of a pedagogical, informative and a communicational type with playful springs of the video game (want to win, collaboration, competition, strategy). This two-dimensional approach has transformed the game from a simple means of entertainment to a robust-integrated tool growing in the world of training and learning. Serious games include the engagement of video games with the worlds of educational and computer simulation to integrate the user in a safe and entertaining learning environment. Many techniques have been used to ameliorate computer graphics and technology in the last few years to make this type of game more adaptive to the learning context. In this study, we are interested in presenting the pedagogical contributions of serious games as well as the different possible approaches of their integration in a learning situation and this is based on a variety of case studies and examples of experimentation. We will start with definitions of other video games that have some valuable characteristics of learning in order to contrast and relate them with serious games. Subsequently, we discuss the definition of serious game and the benefits of its use in education. We will, then, examine approaches to integrate serious games into classrooms with an emphasis on the assets and liabilities of each approach. To finish, we conclude on the trends that will follow the serious games technology in the educational field as well as some recommendations to be taken into consideration in order to better exploit these tools in a pedagogical context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".