An ontology for modelling user’ profiles and activities in gamified education
Bibliographic record
Abstract
Gamification studies in the educational domain usually focus on motivating students to increase their learning performance by enhancing their motivation. Classifications of behavioural profiles are often used for this (referred to as “gamer” or “user types”), which support the personalization of students’ experiences. These classifications consider these profiles from gamers’ or non-gamers’ points of view. However, within education research, it is necessary to broadly inspect these behavioural profiles to create an instructional design based on learners’ intrinsic drivers and motivations. The relationship between these concepts is subjective, complex, and difficult to categorize, demanding research to bridge this gap. Therefore, in this article we present the design and evaluation of an application ontology that seeks to represent relationships between Jung’s archetypes (e.g., the Hero, the Outlaw and others) adapted for educational purposes, creating a new approach for modelling user profiles, a taxonomy of game elements specific for use in educational contexts, and Bloom’s revised taxonomy to classify learning activities types. This ontology enables personalized and instructional designs directly related to the learning activity type for students. We demonstrate that the proposed ontology can help create better gamification designs to support learning, and we envision it to be used both to create unplugged gamification strategies and personalized gamified educational systems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".