Evaluation of quality and personalisation of VR/AR/MR learning systems

Kurilovas, Eugenijus

doi:10.1080/0144929x.2016.1212929

Cited by 74 publications

(40 citation statements)

References 39 publications

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“…According to Kurilovas (2016), learning software and all learning processes should be personalized in accordance with the main characteristics/needs of the learners. Learners have different needs and characteristics, i.e., prior knowledge, intellectual level, interests, goals, cognitive traits (working memory capacity, inductive reasoning ability and associative learning skills), the learning behavioral type (according to the self-regulation level of the individual) and, finally, the learning styles.…”

Section: The Methodology Of Learning Personalization By Applying Intementioning

confidence: 99%

“…According to Kurilovas (2016), learning personalization means creating and implementing personalized learning units/scenarios, which would be based on a recommender system suitable for particular learners in accordance with their personal needs. Educational intelligence supposes an application of intelligent technologies and methods to enable personalized learning for improving learning quality and efficiency.…”

Section: The Methodology Of Learning Personalization By Applying Intementioning

confidence: 99%

“…First of all, according to Kurilovas (2016), a dedicated psychological questionnaire, like the Soloman-Felder Index of Learning Styles Questionnaire, should be applied to obtain the probabilistic learning styles of the students. Thus, a learning styles identification software agent (1) should be developed to obtain these values of student learning styles.…”

Section: The Model Of the Personalized Intelligent Multi-agent Learmentioning

confidence: 99%

“…Second, according to Kurilovas (2016), an open and dynamic learner profile development software agent (2) should be used to create learners' profiles (models) using the results obtained by the agent (1) and adding the other features of learners (knowledge, interests, goals, cognitive traits, learning behavioral type etc. ).…”

Section: The Model Of the Personalized Intelligent Multi-agent Learmentioning

confidence: 99%

See 3 more Smart Citations

Intelektinė daugiaagentė mokymosi sistema, naudojanti edukacinių duomenų tyrybą

Kurilovas¹,

Meleško²,

Krikun³

2018

Self Cite

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show abstract

Section: The Methodology Of Learning Personalization By Applying Intementioning

confidence: 99%

Section: The Methodology Of Learning Personalization By Applying Intementioning

confidence: 99%

Section: The Model Of the Personalized Intelligent Multi-agent Learmentioning

confidence: 99%

Section: The Model Of the Personalized Intelligent Multi-agent Learmentioning

confidence: 99%

See 2 more Smart Citations

Intelektinė daugiaagentė mokymosi sistema, naudojanti edukacinių duomenų tyrybą

Kurilovas¹,

Meleško²,

Krikun³

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to Kurilovas (2016) Kurilovas, Kurilova and Andruskevic (2016), after filling out the Soloman and Felder's Index of Learning Styles Questionnaire, one could obtain, for example, the following learning style, initially stored in his/her student profile/model:…”

Section: Personalization Of Learning Unitsmentioning

confidence: 99%

On the methodology of evaluating the acceptance, use and suitability of personalized learning units

Kurilovas¹,

Minkevičius²,

Kurilova³

et al. 2018

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Nanowire‐Based Soft Wearable Human–Machine Interfaces for Future Virtual and Augmented Reality Applications

Wang

Yap

Gong

et al. 2021

Adv Funct Materials

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A virtual world has now become a reality as augmented reality (AR) and virtual reality (VR) technology become commercially available. Similar to how humans interact with the physical world, AR and VR systems rely on human–machine interface (HMI) sensors to interact with the virtual world. Currently, this is achieved via state of‐the‐art wearable visual and auditory tools that are rigid, bulky, and burdensome, thereby causing discomfort during practical application. To this end, a skin sensory interface has the potential to serve as the next‐generation AR/VR technology because skin‐like wearable sensors have advantages in that they can be ultrathin, ultra‐soft, conformal, and imperceptible, which provides the ultimate comfort and immersive experience for users. In this progress report, nanowire‐based soft wearable HMI sensors including acoustic, strain, pressure sensors, and physiological sensors are reviewed that may be adopted as skin sensory inputs in future AR/VR systems. Further, nanowire‐based soft contact lenses, haptic force, and thermal and vibration actuators are covered as potential means of feedback for future AR/VR systems. Considering the possible effects of the virtual world on human health, skin‐like wearable artery pulses, glucose, and lactate sensors are also described, which may enable imperceptible health monitoring during future AR/VR practices.

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Evaluation of quality and personalisation of VR/AR/MR learning systems

Cited by 74 publications

References 39 publications

Intelektinė daugiaagentė mokymosi sistema, naudojanti edukacinių duomenų tyrybą

Intelektinė daugiaagentė mokymosi sistema, naudojanti edukacinių duomenų tyrybą

On the methodology of evaluating the acceptance, use and suitability of personalized learning units

Nanowire‐Based Soft Wearable Human–Machine Interfaces for Future Virtual and Augmented Reality Applications

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