A course on serious game design and development using an online problem-based learning approach
Bibliographic record
Abstract
Purpose – The purpose of this paper is to describe a novel undergraduate course on serious game design and development that integrates both game and instructional design, thus providing an effective approach to teaching serious game design and development. Very little effort has been dedicated to the teaching of proper serious game design and development leading to many examples of serious games that provide little, if any, educational value. Design/methodology/approach – Organized around a collection of video clips (that provided a brief contextualized overview of the topic and questions for further exploration), readings, interdisciplinary research projects and games, the course introduced the principles of game and instructional design, educational theories used to support game-based learning and methods for evaluating serious games. Discussions and activities supported the problems that students worked on throughout the course to develop a critical stance and approach toward implementing game-based learning. Students designed serious games and examined potential issues and complexities involved in developing serious games and incorporating them within a teaching curriculum. Findings – Results of student course evaluations reveal that the course was fun and engaging. Students found the course fun and engaging, and through the successful completion of the final course project, all students met all of the course objectives. A discussion regarding the techniques and approaches used in the course that were successful (or unsuccessful) is provided. Research limitations/implications – It should be noted that a more detailed analysis has not been presented to fully demonstrate the effectiveness of the course. A more detailed analysis may have included a comparison with, for example, past versions of the course that was not based on an online problem-based learning (PBL) approach, to better quantify the effectiveness of the course. However, such a comparison could not be carried out here, given there was no measure of prior knowledge of students taken before they took course (e.g. no “pre-test data”). Originality/value – Unlike the few existing courses dedicated to serious game design, the course was designed specifically to facilitate a fully online PBL approach and provided students the opportunity to take control of their own learning through active research, exploration and problem-solving alone, in groups and through facilitated class discussions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".