Introduction of Gamification in Common Core Engineering
Bibliographic record
Abstract
The Schulich School of Engineering is currently redesigning its first-year curriculum and will be piloting a number of new approaches in the Fall15/Winter16 academic year. In addition to experiences with flipped classrooms and online professional skills modules, we will be adding a component of gamification to one of our first-year courses. Gamification is the application of the typical elements of game playing (e.g., point scoring, competition with others, rules of play) to education in order to encourage engagement with the course material in a compelling and familiar way. This paper will describe the following: underlying game mechanics; game design techniques; and how these can be integrated into/applied to/used to enhance engineering education. Approaches covered will include the following: using experience points to replace traditional grading; user -generated content; and a tiered rewards system giving students choices that enable them to strategically manipulate their relationship with the course material. Gamification has the ability to let students make choices based on their strengths. Given the four-player archetypes of Explorer, Achiever, Socializer, and Predator, it is important to include incentives that motivate each type of student. Effective gamification achieves not only engagement, but it also attends to cross-archetype engagement. That is, the Socializers will constantly inform the other students of achievements that have been discovered by mainly the Explorers, but when Explorers receive a new achievement, they will feel compelled to become a Socializer and tell everyone of their discovery. Predators might earn an achievement for passing a certain number of people on a leaderboard or for creating a question that was very challenging. They will then feel a sense of ownership and likewise will play the role of Socializer and inform others of their achievement.Examples of ways that gamification can be applied to current practices will be provided.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".