Emotions identification utilizing periodic handwriting on mobile surfaces

Viktors Zagorskis, Atis Kapenieks, Aleksandrs Gorbunovs


The purpose of this study is to between the learners’ emotional characteristics and styles in touch screen environments. We propose a method to identify the variety of learners' boredom in the learning process utilising handwriting data. The novelty of the method is to avoid explicit polling of learners how do they feel. We use the recurring acquiring of personal handwriting data utilising the computational power of both a mobile device and cloud-based resources. Also, we use machine learning-based sentiment detection in the research. We smoothly inject periodic handwriting tests convolving them with learning objects to study the correlation between learners’ emotions dynamic, they demonstrate, and the ability to focus and think critically. With the help of machine-learning methods and new communication protocols, we can step up the student-centric mobile-based education process by taking advantage of the latest achievements in a big data analysis and cloud computing. Also, we clarify the conceptual model for the testbed used in the experiment. The findings may likely impact the future personalized e-learning systems.


Boredom detection; Mobile learning; Handwriting detection; Machine-learning; Online learning

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DOI: http://dx.doi.org/10.21533/pen.v7i1.355


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Copyright (c) 2019 Viktors Zagorskis, Atis Kapenieks, Aleksandrs Gorbunovs

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License