Bibliographic record
Abstract
Purpose The purpose of this paper is to determine the feasibility of modifying a commercial off‐the‐shelf video game that incorporates elements of information literacy. Design/methodology/approach This paper examines six game design elements of educational video games and discusses the resources required to design and build Benevolent Blue, a “modded” video game. Findings This paper provides a discussion of the skills, time and funding required to build a “mod” incorporating information literacy. Research limitations/implications Although modifying commercial videogames is quite popular, very little discussion or work is written about “modding” and its potential use designing video games for libraries. Further research is required to determine if the knowledge transfer of information literacy skills occurs with players. Additional study could look at incorporating information literacy into video games of different genres and well as the impact that video games have on undergraduate student engagement and satisfaction. Practical implications This paper outlines the resources needed to modify a commercial off‐the‐shelf video game and provides suggestions on how others in libraries might do the same. Originality/value This paper looks at serious educational games in a new way – the modification of commercial off the shelf games to develop complete game play experiences that sit outside the classroom and emphasize the importance of play.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".