An ontology for modelling user’ profiles and activities in gamified education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it