دورية أكاديمية
Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response.
| العنوان: | Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response. |
|---|---|
| المؤلفون: | Alqahtani F; School of Computing, Engineering, and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK. fehaid.alqahtani@uws.ac.uk.; Computer Science Department, King Fahad Naval Academy, Jubail 35512, Kingdom of Saudi Arabia. fehaid.alqahtani@uws.ac.uk., Ramzan N; School of Computing, Engineering, and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK. naeem.ramzan@uws.ac.uk. |
| المصدر: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2019 Jan 23; Vol. 19 (3). Date of Electronic Publication: 2019 Jan 23. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Basel, Switzerland : MDPI, c2000- |
| مواضيع طبية MeSH: | Electrocardiography/*methods , Electroencephalography/*methods, Emotions/physiology ; Humans |
| مستخلص: | The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner's emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems. |
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| فهرسة مساهمة: | Keywords: Intelligent Tutoring Systems; electrocardiogram; electroencephalogram; human–computer interaction; physiological signals |
| تواريخ الأحداث: | Date Created: 20190126 Date Completed: 20190212 Latest Revision: 20200225 |
| رمز التحديث: | 20221213 |
| مُعرف محوري في PubMed: | PMC6387072 |
| DOI: | 10.3390/s19030460 |
| PMID: | 30678054 |
| قاعدة البيانات: | MEDLINE |
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