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Physiological State and Learning Ability of Students in Normal and Virtual Reality Conditions: Complexity-Based Analysis

. 2020 Jun 01 ; 22 (6) : e17945. [epub] 20200601

Language English Country Canada Media electronic

Document type Journal Article, Research Support, Non-U.S. Gov't

Links

PubMed 32478661
PubMed Central PMC7313733
DOI 10.2196/17945
PII: v22i6e17945
Knihovny.cz E-resources

BACKGROUND: Education and learning are the most important goals of all universities. For this purpose, lecturers use various tools to grab the attention of students and improve their learning ability. Virtual reality refers to the subjective sensory experience of being immersed in a computer-mediated world, and has recently been implemented in learning environments. OBJECTIVE: The aim of this study was to analyze the effect of a virtual reality condition on students' learning ability and physiological state. METHODS: Students were shown 6 sets of videos (3 videos in a two-dimensional condition and 3 videos in a three-dimensional condition), and their learning ability was analyzed based on a subsequent questionnaire. In addition, we analyzed the reaction of the brain and facial muscles of the students during both the two-dimensional and three-dimensional viewing conditions and used fractal theory to investigate their attention to the videos. RESULTS: The learning ability of students was increased in the three-dimensional condition compared to that in the two-dimensional condition. In addition, analysis of physiological signals showed that students paid more attention to the three-dimensional videos. CONCLUSIONS: A virtual reality condition has a greater effect on enhancing the learning ability of students. The analytical approach of this study can be further extended to evaluate other physiological signals of subjects in a virtual reality condition.

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Kyaw BM, Saxena N, Posadzki P, Vseteckova J, Nikolaou CK, George PP, Divakar U, Masiello I, Kononowicz AA, Zary N, Tudor Car L. Virtual Reality for Health Professions Education: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration. J Med Internet Res. 2019 Jan 22;21(1):e12959. doi: 10.2196/12959. PubMed DOI PMC

Markowitz DM, Laha R, Perone BP, Pea RD, Bailenson JN. Immersive Virtual Reality Field Trips Facilitate Learning About Climate Change. Front Psychol. 2018 Nov 30;9:2364. doi: 10.3389/fpsyg.2018.02364. PubMed DOI PMC

Yin Y. Contact with My Teacher’s Eyes. Phenemol Practice. 2013 Jul 11;7(1):69–81. doi: 10.29173/pandpr20104. DOI

Tarrant J, Viczko J, Cope H. Virtual Reality for Anxiety Reduction Demonstrated by Quantitative EEG: A Pilot Study. Front Psychol. 2018 Jul 24;9:1280. doi: 10.3389/fpsyg.2018.01280. PubMed DOI PMC

Tauscher JP, Wolf Schottky F, Grogorick S, Maximilian Bittner P, Mustafa M, Magnor M. Immersive EEGvaluating Electroencephalography in Virtual Reality. IEEE Conference on Virtual Reality and 3D User Interfaces (VR); 2019; Osaka, Japan. 2019. Aug 15, pp. 1794–1800. DOI

Dey A, Chatburn A, Billinghurst M. Exploration of an EEG-Based Cognitively Adaptive Training System in Virtual Reality. 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR); 2019; Osaka, Japan. 2019. Aug 15, pp. 220–226. DOI

Teo J, Chia JT. EEG-based excitement detection in immersive environments: An improved deep learning approach. 3rd International Conference on Applied Science and Technology (ICAST’18); 2018; Penang, Malaysia. 2018. Apr 10, DOI

Marín-Morales J, Higuera-Trujillo JL, Greco A, Guixeres J, Llinares C, Scilingo EP, Alcañiz M, Valenza G. Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Sci Rep. 2018 Sep 12;8(1):13657. doi: 10.1038/s41598-018-32063-4. PubMed DOI PMC

Johnson S. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. New York: Scribner; 2012.

Kiew CL, Brahmananda A, Islam KT, Lee HN, Vernier SA, Saraar A, Namazi H. Complexity-based analysis of the relation between tool wear and machine vibration in turning operation. Fractals. 2020 Feb 03;28(01):2050018. doi: 10.1142/s0218348x20500188. DOI

Kiew Cl, Brahmananda A, Islam KT, Lee HN, Venier SA, Saraar A, Namazi H. Analysis of the relation between fractal structures of machined surface and machine vibration signal in turning operation. Fractals. 2020 Feb 07;28(01):2050019. doi: 10.1142/s0218348x2050019x. DOI

Namazi H, Jafari S. Decoding of wrist movements’ direction by fractal analysis of magnetoencephalography (MEG) signal. Fractals. 2019 May 30;27(02):1950001. doi: 10.1142/s0218348x19500014. DOI

Omam S, Babini MH, Sim S, Tee R, Nathan V, Namazi H. Complexity-based decoding of brain-skin relation in response to olfactory stimuli. Comput Methods Programs Biomed. 2020 Feb;184:105293. doi: 10.1016/j.cmpb.2019.105293. PubMed DOI

Tapanainen JM, Thomsen PEB, Køber L, Torp-Pedersen C, Mäkikallio TH, Still A, Lindgren KS, Huikuri HV. Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. Am J Cardiol. 2002 Aug 15;90(4):347–352. doi: 10.1016/s0002-9149(02)02488-8. PubMed DOI

Namazi RH. Fractal-based analysis of the influence of music on human respiration. Fractals. 2017 Nov 21;25(06):1750059. doi: 10.1142/s0218348x17500591. DOI

Mozaffarilegha M, Namazi H, Tahaei AA, Jafari S. Complexity-Based Analysis of the Difference Between Normal Subjects and Subjects with Stuttering in Speech Evoked Auditory Brainstem Response. J Med Biol Eng. 2018 Jun 1;39(4):490–497. doi: 10.1007/s40846-018-0430-x. DOI

Alipour H, Towhidkhah F, Jafari S, Menon A, Namazi H. Complexity-Based Analysis of the Relation Between Fractal Visual Stimuli and Fractal Eye Movements. Fluct Noise Lett. 2019 Jul 16;18(03):1950012. doi: 10.1142/s0219477519500123. DOI

Namazi H, Akrami A, Hussaini J, Silva ON, Wong A, Kulish VV. The fractal based analysis of human face and DNA variations during aging. Biosci Trends. 2017 Jan 16;10(6):477–481. doi: 10.5582/bst.2016.01182. PubMed DOI

Alipour ZM, Khosrowabadi R, Namazi H. Fractal-based analysis of the influence of variations of rhythmic patterns of music on human brain response. Fractals. 2018 Oct 30;26(05):1850080. doi: 10.1142/s0218348x18500809. DOI

Namazi H, Khosrowabadi R, Hussaini J, Habibi S, Farid AA, Kulish VV. Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal. Oncotarget. 2016 Aug 30;7(35):56120–56128. doi: 10.18632/oncotarget.11234. PubMed DOI PMC

Kawano K. Electroencephalography and Its Fractal Analysis During Olfactory Stimuli. Olfaction and Taste XI; Proceedings of the 11th International Symposium on Olfaction and Taste and of the 27th Japanese Symposium on Taste and Smell; July 12-16, 1993; Kaikan, Sapporo, Japan. 1994. pp. 668–672. DOI

Namazi H, Ala TS, Bakardjian H. Decoding of steady-state visual evoked potentials by fractal analysis of the electroencephalographic (EEG) signal. Fractals. 2019 Jan 15;26(06):1850092. doi: 10.1142/s0218348x18500925. DOI

Ahmadi-Pajouh MA, Ala TS, Zamanian F, Namazi H, Jafari S. Fractal-based classification of human brain response to living and non-living visual stimuli. Fractals. 2018 Oct 30;26(05):1850069. doi: 10.1142/s0218348x1850069x. DOI

Namazi H, Aghasian E, Ala TS. Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia. Technol Health Care. 2019;27(3):233–241. doi: 10.3233/THC-181497. PubMed DOI

Namazi H, Ala TS, Kulish V. Decoding of upper limb movement by fractal analysis of electroencephalogram (EEG) signal. Fractals. 2018 Oct 30;26(05):1850081. doi: 10.1142/s0218348x18500810. DOI

Kamal SM, Sim S, Tee R, Nathan V, Aghasian E, Namazi H. Decoding of the relationship between human brain activity and walking paths. Technol Health Care. 2019 Nov 14;:Epub ahead of print. doi: 10.3233/THC-191965. PubMed DOI

Namazi H, Jafari S. Age-based variations of fractal structure of EEG signal in patients with epilepsy. Fractals. 2018 Sep 25;26(04):1850051. doi: 10.1142/s0218348x18500512. DOI

Namazi H. Fractal-based classification of electromyography (EMG) signal in response to basic movements of the fingers. Fractals. 2019 May 30;27(03):1950037. doi: 10.1142/s0218348x19500373. DOI

Namazi H. Fractal-based classification of electromyography (EMG) signal between fingers and hand’s basic movements, functional movements, and force patterns. Fractals. 2019 Jul 11;27(04):1950050. doi: 10.1142/s0218348x19500506. DOI

Namazi H. Decoding of hand gestures by fractal analysis of electromyography (EMG) signal. Fractals. 2019 May 30;27(03):1950022. doi: 10.1142/s0218348x19500221. DOI

Namazi H, Jafari S. Decoding of simple hand movements by fractal analysis of electromyography (EMG) signal. Fractals. 2019 Jul 11;27(04):1950042. doi: 10.1142/s0218348x19500427. DOI

Kamal SM, Sim S, Tee R, Nathan V, Namazi H. Complexity-Based Analysis of the Relation between Human Muscle Reaction and Walking Path. Fluct Noise Lett. 2020 Jan 28;:2050025. doi: 10.1142/s021947752050025x. PubMed DOI

Arjunan SP, Kumar DK. Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018; Honolulu, HI. 2018. Oct 29, pp. 5656–5659. PubMed DOI

Li J, Du Q, Sun C. An improved box-counting method for image fractal dimension estimation. Pattern Recognition. 2009 Nov;42(11):2460–2469. doi: 10.1016/j.patcog.2009.03.001. DOI

Qadri MO, Namazi H. Fractal-Based Analysis of the Relation Between Surface Finish and Machine Vibration in Milling Operation. Fluct Noise Lett. 2019 Jun 28;19(01):2050006. doi: 10.1142/s0219477520500066. DOI

Kulish V, Sourin A, Sourina O. Human electroencephalograms seen as fractal time series: mathematical analysis and visualization. Comput Biol Med. 2006 Mar;36(3):291–302. doi: 10.1016/j.compbiomed.2004.12.003. PubMed DOI

Rezvani S, Wang X, Pourpanah F. Intuitionistic Fuzzy Twin Support Vector Machines. IEEE Trans Fuzzy Syst. 2019 Nov;27(11):2140–2151. doi: 10.1109/tfuzz.2019.2893863. DOI

Pourpanah F, Lim CP, Hao Q. A reinforced fuzzy ARTMAP model for data classification. Int J Mach Learn Cyber. 2018 Jun 15;10(7):1643–1655. doi: 10.1007/s13042-018-0843-4. DOI

Pourpanah F, Zhang B, Ma R, Hao Q. Anomaly Detection and Condition Monitoring of UAV Motors and Propellers. 2018 IEEE Sensors; October 28-31, 2018; New Delhi, India. 2018. Dec 27, pp. 1–4. DOI

Namazi H, Kulish VV. Fractional Diffusion Based Modelling and Prediction of Human Brain Response to External Stimuli. Comput Math Methods Med. 2015;2015:148534. doi: 10.1155/2015/148534. PubMed DOI PMC

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