Virtual Reality Assistant Technology for Learning Primary Geography

Lv, Zhihan; Li, Xiaoming

doi:10.1007/978-3-319-32865-2_4

Cited by 14 publications

(14 citation statements)

References 57 publications

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“…Jill Watson (Goel & Polepeddi, 2016), Nicky (Kincaid & Pollock, 2017), Maria (Rajagopal & Babu, 2018) PseduoEye (Daraghmi & Yosef, 2016), LABTA (Yang, 2010), BoBi (Liu & Zhu, 2017), Project Nethra (Weeratunga, Jayawardana, Hasindu, Prashan, & Thelijjagoda, 2015), LiSa (Dibitonto, Leszczynska, Tazzi, & Medaglia, 2018), and Scarlet (Ilhan, Music, Junuz, & Mirza, 2017) are some of the SPDAs specifically designed for educational purposes. There are also studies (Bravo, Paliyawan, Harada, & Thawonmas, 2017;Huang, Chang, Chen, & Chen, 2014;Lv & Li, 2015) that use the body-motion preference systems such as eye-tracking, kinect and VR headsets. In two studies (Bogdan, Yurchenko, Bailo, Rameau, Yoo, & Kweon, 2017;Bouloutian & Kim, 2014) the SPDAs are integrated to wearable technologies.…”

Section: Game-related Findingsmentioning

confidence: 99%

From Robo-Sapiens to Robo-Ludens: Re-Thinking Future Learning With Smart Personal Digital Playmates

Sezgin¹

2020

Turkish Online Journal of Distance Education

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This systematic review investigates two foci: identifying the ongoing status of smart personal digital assistants in educational contexts and the possible relationship between smart personal digital assistants and gamification elements. This relationship is expressed in almost two new figurative terms, namely, robosapiens and robo-ludens and the present study lays on the foundations of these terms. Therefore, this paper presents a holistic approach for the review and synthesis of the previous literature. The data of the study were gathered based on the findings reported in the related research articles, conference papers, dissertations, book chapters and project reports by focusing on 9 sets of generic keywords. As a part of this study, smart personal digital assistants in learning processes were scanned purposively in the lens of gamification. To this end, a two step inclusion criteria was implemented to review the studies of the research scope. Content analysis was the main method used for data analysis. The research findings revealed the most significant fields for the use of smart personal digital assistants are dominantly e-learning and special education. The research also discusses game-related components in smart personal digital assistant use in edu-centric purposes.

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Section: Game-related Findingsmentioning

confidence: 99%

From Robo-Sapiens to Robo-Ludens: Re-Thinking Future Learning With Smart Personal Digital Playmates

Sezgin¹

2020

Turkish Online Journal of Distance Education

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“…Given the rapid development of innovation, the VR system has evolved with mobile implementation that comes along with specially designed wearable technologies to enable users to experience immersive VR [1]. The evolution of mobile implementation (e.g., smartphones and tablets) into VR is influenced by the advancement state of mobile intelligence and performance specifications; moreover, it is highly influenced by different mass users because of its convenience to perform daily activities [1,2].…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality Usability and Accessibility for Cultural Heritage Practices: Challenges Mapping and Recommendations

et al. 2021

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In recent years, virtual reality (VR) is at its maturity level for real practical exercises amongst many fields of studies, especially in the virtual walkthrough exploration system of cultural heritage (CH). However, this study remains scattered and limited. This work presents a systematic review that maps out the usability and accessibility issues that are challenging in using VR in CH. We identified 45 challenges that are mapped into five problem groups: system design, development process, technology, assessment process and knowledge transfer. This mapping is then used to propose 58 recommendations to improve the usability and accessibility of VR in CH that are categorized in three different recommendation groups namely, discovery and planning, design and development, and finally the assessment factors. This analysis identified the persistence in certain accessibility and usability problems such as there is a limit in navigating the view and space that constraint the users’ free movement and the navigation control is not ideal with the keyboard arrow button. This work is important because it provides an overview of usability and accessibility based challenges that are faced in applying, developing, deploying and assessing VR in the usage of digitalizing CH and proposed a great number of constructive recommendations to guide future studies. The main contribution of this paper is the mapping of usability and accessibility challenges into categories and the development of recommendations based on the identified problems.

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“…These components are used in the process of planning, implementing, evaluating, following-up and developing objectives [1]. Machine learning has become a new frontier for higher education.…”

Section: Introductionmentioning

confidence: 99%

“…There are different ways of using machine learning technology in education, such as in providing diverse learning options so a learner can discover what suits him/her best but in a manner where all individual variances between pupils are considered. Machine learning can also be used in review a lesson that was hard to understand [1]. Machine learning in education works in harmony with students' needs, and at a time and place that suits them best.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning in Educational Technology

Nafea¹

2018

Machine Learning - Advanced Techniques and Emerging Applications

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Machine learning is a subset of artificial intelligence (AI) that helps computers or teaching machines learn from all previous data and make intelligent decisions. The machine-learning framework entails capturing and maintaining a rich set of information and transforming it into a structured knowledge base for different uses in various fields. In the field of education, teachers can save time in their non-classroom activities by adopting machine learning. For example, teachers can use virtual assistants who work remotely from the home for their students. This kind of assistance helps to enhance students' learning experience and can improve progression and student achievement. Machine learning fosters personalized learning in the context of disseminating education. Advances in AI are enabling teachers to gain a better understanding of how their students are progressing with learning. This enables teachers to create customized curriculum that suits the specific needs of the learners. When employed in the context of education, AI can foster intelligence moderation. It is through this platform that the analysis of data by human tutors and moderators is made possible.

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Virtual Reality Assistant Technology for Learning Primary Geography

Cited by 14 publications

References 57 publications

From Robo-Sapiens to Robo-Ludens: Re-Thinking Future Learning With Smart Personal Digital Playmates

From Robo-Sapiens to Robo-Ludens: Re-Thinking Future Learning With Smart Personal Digital Playmates

Virtual Reality Usability and Accessibility for Cultural Heritage Practices: Challenges Mapping and Recommendations

Machine Learning in Educational Technology

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