学生の満足度に付随したヴァーチャルラーニングにおける有効性向上の創造と非言語的行動についての調査

国立大学法人長岡技術科学大

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications.Comment: [Accepted version] In Proceedings of CHI Conference on Human Factors in Computing Systems (CHI '21), May 8-13, 2021, Yokohama, Japan. ACM, New York, NY, USA. 19 Pages. https://doi.org/10.1145/3411764.344557

You Can't Hide Behind Your Headset: User Profiling in Augmented and Virtual Reality

Virtual and Augmented Reality (VR, AR) are increasingly gaining traction thanks to their technical advancement and the need for remote connections, recently accentuated by the pandemic. Remote surgery, telerobotics, and virtual offices are only some examples of their successes. As users interact with VR/AR, they generate extensive behavioral data usually leveraged for measuring human behavior. However, little is known about how this data can be used for other purposes. In this work, we demonstrate the feasibility of user profiling in two different use-cases of virtual technologies: AR everyday application ($N=34$) and VR robot teleoperation ($N=35$). Specifically, we leverage machine learning to identify users and infer their individual attributes (i.e., age, gender). By monitoring users' head, controller, and eye movements, we investigate the ease of profiling on several tasks (e.g., walking, looking, typing) under different mental loads. Our contribution gives significant insights into user profiling in virtual environments

Emotion Detection Research: A Systematic Review Focuses on Data Type, Classifier Algorithm, and Experimental Methods

There is a lot of research being done on detecting human emotions. Emotion detection models are developed based on physiological data. With the development of low-cost wearable devices that measure human physiological data such as brain activity, heart rate, and skin conductivity, this research can be conducted in developing countries like Southeast Asia. However, as far as the author's research is concerned, a literature review has yet to be found on how this research on emotion detection was carried out in Southeast Asia. Therefore, this study aimed to conduct a systematic review of emotion detection research in Southeast Asia, focusing on the selection of physiological data, classification methods, and how the experiment was conducted according to the number of participants and duration. Using PRISMA guidelines, 22 SCOPUS-indexed journal articles and proceedings were reviewed. The review found that physiological data were dominated by brain activity data with the Muse Headband, followed by heart rate and skin conductivity collected with various wristbands, from around 5-31 participants, for 8 minutes to 7 weeks. Classification analysis applies machine learning, deep learning, and traditional statistics. The experiments were conducted primarily in sitting and standing positions, conditioned environments (for developing research), and unconditioned environments (applied research). This review concluded that future research opportunities exist regarding other data types, data labeling methods, and broader applications. These reviews will contribute to the enrichment of ideas and the development of emotion recognition research in Southeast Asian countries in the future

Pengaruh Kualitas Pengajaran, Kualitas Pelayanan akademik dan Lingkungan Belajar Virtual pada Kepuasan Mahasiswa Pascasarjana dalam Perkuliahan Daring

Penting bagi perguruan tinggi untuk memperhatikan kepuasan pengguna jasa pendidikan karena hal ini berkontribusi besar pada kemajuan perguruan tinggi. Kepuasan mahasiswa terhadap pembelajaran daring dipengaruhi oleh kualitas pengajaran, kualitas pelayanan akademik, dan lingkungan belajar. Mahasiswa yang merasa puas dengan ketiga faktor antara lain kualitas pengajaran, kualitas pelayanan akademik, dan lingkungan belajar cenderung memiliki tingkat kepuasan yang lebih tinggi. Namun, masih terdapat kesenjangan penelitian yang perlu diisi, terutama dalam hal mengukur pengaruh masing-masing faktor secara lebih spesifik dan menyeluruh, serta mempertimbangkan faktor lain yang dapat mempengaruhi kepuasan mahasiswa. Tujuan penelitian ini yaitu (1) Untuk mengetahui dan menganalisis pengaruh kualitas pengajaran terhadap kepuasan mahasiswa, (2) Untuk mengetahui dan menganalisis pengaruh kualitas pelayanan akademik terhadap kepuasan mahasiswa, (3) Untuk mengetahui dan menganalisis pengaruh lingkungan belajar terhadap kepuasan mahasiswa, (4) Untuk mengetahui dan menganalisis pengaruh kualitas pengajaran, kualitas pelayanan akademik dan lingkungan belajar secara bersamaan terhadap kepuasan mahasiswa. Jenis penelitian ini adalah penelitian kausalitas dengan pendekatan penelitian metode kuantitatif. Populasi dalam penelitian ini adalah 119 mahasiswa pasca sarjana dengan sampel yang digunakan adalah 30 mahasiswa pasca sarjana. Teknik analisis data yang digunakan adalah uji instrumen, uji asumsi klasik dan uji hipotesis. Hasil penelitian menunjukkan bahwa (1) Kualitas pengajaran berpengaruh secara parsial terhadap kepuasan mahasiswa, (2) Kualitas pelayanan akademik secara parsial berpengaruh terhadap kepuasan mahasiswa, (3) Lingkungan belajar secara parsial berpengaruh terhadap kepuasan mahasiswa, dan (4) Kualitas pengajaran, kualitas pelayanan akademik dan lingkungan belajar secara bersama-sama berpengaruh terhadap kepuasan mahasiswa.Penting bagi perguruan tinggi untuk memperhatikan kepuasan pengguna jasa pendidikan karena hal ini berkontribusi besar pada kemajuan perguruan tinggi. Kepuasan mahasiswa terhadap pembelajaran daring dipengaruhi oleh kualitas pengajaran, kualitas pelayanan akademik, dan lingkungan belajar. Mahasiswa yang merasa puas dengan ketiga faktor antara lain kualitas pengajaran, kualitas pelayanan akademik, dan lingkungan belajar cenderung memiliki tingkat kepuasan yang lebih tinggi. Namun, masih terdapat kesenjangan penelitian yang perlu diisi, terutama dalam hal mengukur pengaruh masing-masing faktor secara lebih spesifik dan menyeluruh, serta mempertimbangkan faktor lain yang dapat mempengaruhi kepuasan mahasiswa. Tujuan penelitian ini yaitu (1) Untuk mengetahui dan menganalisis pengaruh kualitas pengajaran terhadap kepuasan mahasiswa, (2) Untuk mengetahui dan menganalisis pengaruh  kualitas pelayanan akademik terhadap kepuasan mahasiswa, (3) Untuk mengetahui dan menganalisis pengaruh lingkungan belajar terhadap kepuasan mahasiswa, (4) Untuk mengetahui dan menganalisis pengaruh kualitas pengajaran, kualitas pelayanan akademik dan lingkungan belajar secara bersamaan terhadap kepuasan mahasiswa. Jenis penelitian ini adalah penelitian kausalitas dengan pendekatan penelitian metode kuantitatif. Populasi dalam penelitian ini adalah 119 mahasiswa pasca sarjana dengan sampel yang digunakan adalah 30 mahasiswa pasca sarjana. Teknik analisis data yang digunakan adalah uji instrumen, uji asumsi klasik dan uji hipotesis. Hasil penelitian menunjukkan bahwa (1) Kualitas pengajaran berpengaruh secara parsial terhadap kepuasan mahasiswa, (2) Kualitas pelayanan akademik secara parsial berpengaruh terhadap kepuasan mahasiswa, (3) Lingkungan belajar secara parsial berpengaruh terhadap kepuasan mahasiswa, dan (4) Kualitas pengajaran, kualitas pelayanan akademik dan lingkungan belajar secara bersama-sama berpengaruh terhadap kepuasan mahasiswa

Is there Joy Beyond the Joystick?: Immersive Potential of Brain-Computer Interfaces

Immersion, the state of being fully engaged in one\u27s current operation, is a descriptor commonly used to appraise user experience in computer games and software applications. As the use of brain-computer interfaces (BCIs) begins to expand into the consumer sphere, questions arise concerning the ability of BCIs to modulate user immersion. This study employed a computer game to examine the effect of a consumer-grade BCI (the Emotiv EPOC) on immersion. In doing so, this study also explored the relationship between BCI usability and immersion levels. An experiment with twenty-seven participants showed that users were significantly more immersed when controlling the testing game with a BCI in comparison to traditional control methods. The results suggest that increased immersion levels may be caused by the challenging nature of BCI control rather than the BCI\u27s ability to directly translate user intent

Attention, concentration, and distraction measure using EEG and eye tracking in virtual reality

Attention is important in learning, Attention-deficit/hyperactivity disorder, Driving, and many other fields. Hence, intelligent tutoring systems, Attention-deficit/hyperactivity disorder diagnosis systems, and distraction detection of driver systems should be able to correctly monitor the attention levels of individuals in real time in order to estimate their attentional state. We study the feasibility of detecting distraction and concentration by monitoring participants' attention levels while they complete cognitive tasks using Electroencephalography and Eye Tracking in a virtual reality environment. Furthermore, we investigate the possibility of improving the concentration of participants using relaxation in virtual reality. We developed an indicator that estimates levels of attention with a real value using EEG data. The participant-independent indicator based on EEG data we used to assess the concentration levels of participants correctly predicts the concentration state with an accuracy (F1 = 73%). Furthermore, the participant-independent distraction model based on Eye Tracking data correctly predicted the distraction state of participants with an accuracy (F1 = 89%) in a participant-independent validation setting.La concentration est importante dans l’apprentissage, Le trouble du déficit de l’attention avec ou sans hyperactivité, la conduite automobile et dans de nombreux autres domaines. Par conséquent, les systèmes de tutorat intelligents, les systèmes de diagnostic du trouble du déficit de l’attention avec ou sans hyperactivité et les systèmes de détection de la distraction au volant devraient être capables de surveiller correctement les niveaux d’attention des individus en temps réel afin de déduire correctement leur état attentionnel. Nous étudions la faisabilité de la détection de la distraction et de la concentration en surveillant les niveaux d’attention des participants pendant qu’ils effectuent des tâches cognitives en utilisant l’Électroencéphalographie et l’Eye Tracking dans un environnement de réalité virtuelle. En outre, nous étudions la possibilité d’améliorer la concentration des participants en utilisant la relaxation en réalité virtuelle. Nous avons mis au point un indicateur qui estime les niveaux d’attention avec une valeur réelle en utilisant les données EEG. L’indicateur indépendant du participant basé sur les données EEG que nous avons utilisé pour évaluer les niveaux de concentration des participants prédit correctement l’état de concentration avec une précision (F1 = 73%). De plus, le modèle de distraction indépendant des participants, basé sur les données d’Eye Tracking, a correctement prédit l’état de distraction des participants avec une précision (F1 = 89%) dans un cadre de validation indépendant des participants