Serious Games in Engineering: The Current State, Trends, and Future
Bibliographic record
Abstract
Since its inception in the late 20 th Century, computer graphics have improved exponentially and is improving even further in new avenues.While arcade games were early adopters of computer graphics, it was really in the 1990s, with the advent of the personal computer, that video games really started to gain traction.The video gaming industry started humbly with an online community of recreational developers.However, the internet bubble saw companies investing heavily in this new medium for games.Today, the video gaming industry is worth closer to $150 billion USD of yearly revenue, with well established practices, trends and new genres [1].Furthermore, video games have delivered a wide variety of experiences, from interactive story telling, open world exploration, social games, puzzle games, virtual reality games, mobile games and so on.The present paper seeks to provide a direct comparison of trends in the video gaming industry, and how it could be translated to Serious Games in Engineering Education.To this aim, 28 relevant studies which have reported games for teaching engineering courses within the past decade were investigated.These studies were obtained after extensive Scopus search queries and filtered manually according to 8 research questions.Key questions we seek to investigate are what genre of games are being employed, disciplines most often targeted for gamification, assessment tools used to gather data on student learning within gamified settings, learning outcomes and attitudes towards game modules for students' engineering courses and as well as data analysis/collection methods.The results indicated that computer engineering and mechanical engineering disciplines were most used in serious games in engineering education.Unique concepts/topics were addressed is all the 28 articles reviewed.Questionnaire and pre-and post-tests were the most preferred data collected tools.20 out of 28 articles used convenience sampling as the sampling method and most articles used sample sizes less than 100.Most articles used descriptive analyses methods in analyzing the data.Simulation was reported as the most used game genres and web-based application game platforms was commonly used in serious games in engineering education.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".