Figure 1: A) Participants seeing reality through head-mounted displays.B) Co-located interactive objects.C) Individual contentgeneration panel.D) 3D mid-air drawing.E) A virtual object transitioning to physical space from VR through the portal.

Designing affect-based personalized technology involves dealing with large datasets. Machine learning (ML) algorithms are employed to predict affect and similar human factors from in-game metrics, behavioral patterns, or physiological responses. The classification performance is usually presented as a global point estimate without providing user-specific interpretations. This approach is incompatible with effective personalization in games because it disregards the variability of body responses between players. This paper proposes a methodology to classify subjective human factors from large multimodal data. A public VR dataset (CEAP-360VR) was used to extensively compare three ML classifiers and five feature importance techniques. The produced models could reduce the original feature space by 82% (from 113 to 20 features) without compromising predictive performance (F1 score). A random forest (RF) using forward sequential feature selection (fSFS) yielded the best prediction of binary valence (F1=0.761) and arousal (F1=0.748). Finally, feature importance rankings are discussed with emphasis on global and user-specific patterns that may improve affect recognition. The proposed methodology is envisioned to help game designers and researchers create customized user-centric games and VR experiences inferring possible explanations from multimodal datasets.

This paper presents a novel approach to predict caries development in dental patients by analyzing event interval sequences extracted from electronic health records (EHRs). Leveraging a subset of the SKaPa dataset, comprising 1,500 patients aged 30 to 70, and encompassing 14,870 tooth-wise event interval sequences, our method surpasses baseline models and state-of-the-art deep learning approaches. By assessing temporal relations between event intervals and utilizing interpretable classification models such as decision trees (DTs) and random forests (RFs), our approach achieves higher recall rates and area under the precision-recall curve (AUPRC) scores in identifying cases of caries development. Notably, our methods demonstrate superior performance in learning the minority class (i.e., caries development), underscoring the effectiveness of the event interval representation in capturing predictive features. These findings underscore the potential of our approach to improve caries prognosis and enable targeted interventions in dental healthcare.

In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.