A Recommender System and Survey for Tailored Gamification in Digital Education
| Vortragende(r) | Anna Katharina Ricker | |
|---|---|---|
| Vortragstyp | Masterarbeit | |
| Betreuer(in) | Lucia Happe | |
| Termin | Di 17. Juni 2025, 16:00 (Raum 010 (Gebäude 50.34)) | |
| Vortragssprache | Deutsch | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | Gamification in digital education is well-studied, yet many approaches remain generic, ignoring individual and contextual differences. This thesis introduces a framework for tailored gamification with three main contributions: (1) a taxonomy of 13 gamification elements, (2) a rule-based, evidence-weighted recommender system ranking elements by user and context parameters, and (3) a user study (N=527) across six variables, including 34% minors.
The recommender employs a novel algorithm to normalize heterogeneous literature data, prioritizing interpretability over opaque machine learning. The study finds age to be the strongest predictor ($\eta^2$ = 0.05), while learning style explains less than 2% of the variance. Age-based groups were derived to enable consistent future recommendations and reveal non-linear preference patterns. Recommender output strongly aligned with age-based preferences (Spearman $\rho$ = 0.80). Other parameters showed weaker correlations, highlighting opportunities for improvement through better data aggregation and integration of survey-based insights. | |