A Video Game for Entrepreneurship Learning in Ecuador: Development Study
Bibliographic record
Abstract
BACKGROUND: Games have been a part of human life since ancient times and are taught to children and adults who want to simultaneously have fun and learn. Nevertheless, in the third decade of this century, technology invites us to consider using video games to learn topics such as entrepreneurship. However, developing a serious game (SG) is difficult because everyone who forms part of the game development team requires adequate learning resources to acquire the necessary information and improve their game development skills. OBJECTIVE: This work aimed to detail the experience gained in developing ATIC (Aprende, Trabaja, Innova, Conquista [learn, work, innovate, conquer]), an SG proposed for teaching and learning entrepreneurship. METHODS: To develop a videogame, first, we established a game development team formed by professors, professionals, and students who have different roles in this project. Scrum was adopted as a project management method. To create concept art for the video game, designers collected ideas from various games, known as "getting references." In contrast, narratology considers the life of a recent university graduate immersed in real life, considering locations, characteristics, and representative characters from an essential city of Ecuador. RESULTS: In a Unity 3D video game in ATIC, the life of a university student who graduates and ventures into a world full of opportunities, barriers, and risks, where the player needs to make decisions, is simulated. The art of this video game, including sounds and music, is based on the landscape and characteristics of and characters from Cuenca, Ecuador. The game aims to teach entrepreneurs the mechanisms and processes to form their businesses. Thus, we developed the following elements of an SG: (1) world, (2) objects, (3) agents, and (4) events. CONCLUSIONS: The narrative, mechanics, and art of video games are relevant. However, project management tools such as leaderboards and appointments are crucial to influencing individuals' decision to continue to play, or not play, an SG. Developing a serious video game is not an easy task. It was essential to consider many factors, such as the video game audience, needs of learning, context, similarities with the real world, narrative, game mechanics, game art, and game sounds. However, overall, the primary purpose of a serious video game is to transmit knowledge in a fun way and to give adequate and timely feedback to the gamer. Finally, nothing is possible if the members of game development team are not satisfied with the project and not clear about their roles.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".