2018
DOI: 10.1007/s10055-018-0363-2
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Virtual and augmented reality effects on K-12, higher and tertiary education students’ twenty-first century skills

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Cited by 261 publications
(172 citation statements)
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References 57 publications
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“…These possibilities are expressed in a general sense and rarely refer to specific teaching situations or experiences, but can be characterised as an 'envisioned potential' of HMD VR. These are signs that VR (and AR) is not simply a 'hype' and that it can be used to meet these kinds of expectations (Bom 2017;Papanastasiou et al 2019). However, as is often the case when new technologies emerge, their potential can be overestimated and the hype around them exaggerated (van Lente et al 2013;cf.…”
Section: Discussionmentioning
confidence: 99%
“…These possibilities are expressed in a general sense and rarely refer to specific teaching situations or experiences, but can be characterised as an 'envisioned potential' of HMD VR. These are signs that VR (and AR) is not simply a 'hype' and that it can be used to meet these kinds of expectations (Bom 2017;Papanastasiou et al 2019). However, as is often the case when new technologies emerge, their potential can be overestimated and the hype around them exaggerated (van Lente et al 2013;cf.…”
Section: Discussionmentioning
confidence: 99%
“…For the physical manifestation of emotions, although human brain waves and heart rate are a good basis for emotional discrimination [9]. Brengman classified human emotions by studying the universal meaning of facial expressions and exploring the association between expressions and a definite emotion beforehand, and proposed six basic emotions: happiness, sadness, surprise, fear, disgust, and anger, each of which is all of them correspond to their unique facial features [10].…”
Section: Introductionmentioning
confidence: 99%
“…Elmqaddem N et al designed an anomaly detection method based on behavior patterns to detect the medical smartphone network (MSN) and judge the credibility of the node by identifying the difference in Euclidean distance between two behavior characteristics [24]. Papanastasiou G et al proposed IoT, a system for detecting infected IoT devices [25]. Compared with previous work, IoT uses a novel self-learning method to classify devices into device types and establish a normal communication configuration for each device, which can then be used to detect abnormal deviations in the communication mode [26].…”
Section: Introductionmentioning
confidence: 99%