Promotion of Learning Motivation through Individualization of Learner-Game Interaction

Kaczmarek, Sandra; Petroviča, Sintija

doi:10.1109/cig.2018.8490371

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…Burgeoning research on physiological interfaces revolutionizes novel methods for digital play; player's mode of input [4], adaptability [5], player's affective state evolution [6], gaming experiences [7], and live streaming for inter-player communications [8]. The advent of self-tracking wearables for physiological data acquisition and feedback to the user in real-time via off-the-shelf gadgets such as the Fitbit 1 & Apple Watch 2 has mirrored the rising popularity and converged the ever-expanding affective computing research and incorporated physiological self-tracking into VR games [9].…”

Section: A Backgroundmentioning

confidence: 99%

“…These proliferating improvements in hardware and software led to the integration of various physiological signals into the digital gaming space as in Unity 3D 3 , Source SDK 4 , and Unreal Engine 5 . Physiological sensing using Empatica E4 wristband 6 , Affdex Software Development Kit (SDK) by Affectiva [27], and differentiation of positive and negative events using Microsoft Xbox's facial EMG [28] are prominent outcomes.…”

Section: Related Workmentioning

confidence: 99%

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Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

Pratiher¹,

Radhakrishnan²,

Sahoo³

et al. 2021

Preprint

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"This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible." Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.

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Section: A Backgroundmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

Pratiher¹,

Radhakrishnan²,

Sahoo³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…________________________________________________________________________________________________ 2018/23 103 adaptation to the student's emotions [13]), that are in detail identified and discussed in the further sections.…”

Section: Applied Computer Systemsmentioning

confidence: 99%

“…An integration of various sensors providing data about student's emotional state, for example, physiological sensors (e.g., skin conductivity sensor, heart rate sensor or electromyograph) or observational sensors (e.g., video cameras, eye trackers or microphones) improves the accuracy of emotion recognition. However, previous experience acquired during empirical evaluation of the developed emotionally intelligent tutoring system has shown that observations demonstrate that fear and negative attitude still exist regarding affect-aware technologies and not all students are open to the analysis of their emotional data, even if data is acquired using video cameras [13]. Therefore, other methods for the emotion identification, which do not influence a student in an intrusive way (e.g., analysis of interaction data or use of input devices), should be considered for the integration in learning environments.…”

Section: A Emotional State Acquisitionmentioning

confidence: 99%

Challenges in the Development of Affective Collaborative Learning Environment with Artificial Peers

Pudāne

Petroviča

Lavendelis

et al. 2018

Applied Computer Systems

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Collaborative learning is a process that involves a group of peers collaborating with the aim to acquire new knowledge or skills. Collaborative learning environment enables such interactions by means of ICT. The paper focuses on affective collaborative learning environments, i.e., collaborative learning environments that are additionally aware of user’s emotions and moods. Based on the analysis of existing research, a general architecture of an affective collaborative learning environment has been proposed in the paper and the main challenges for developing such an environment have been identified, namely, nonintrusive and safe detection of user’s emotions, the adaptation of tutoring strategies, as well as modelling of artificial peers. This study can be considered the first step for the development of the collaborative learning environment that takes into account various affective aspects during the collaborative learning process.

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"Let's Get Physiological, Physiological!"

Robinson

Wiley

Rezaeivahdati

et al. 2020

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

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Promotion of Learning Motivation through Individualization of Learner-Game Interaction

Cited by 3 publications

References 34 publications

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

Challenges in the Development of Affective Collaborative Learning Environment with Artificial Peers

"Let's Get Physiological, Physiological!"

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