Intelligent Virtual Reality Tutoring System Supporting Open Educational Resource Access

Ahn, Jae‐wook; Tejwani, Ravi; Sundararajan, Sharad; Sipolins, Aldis; O’Hara, Sean; Paul, Anand; Kokku, Ravi; Kjallstrom, Jan; Dang, Nam Hai; Huang, Yazhou

doi:10.1007/978-3-319-91464-0_28

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…4. The pedagogical approach (behaviorism, constructivism, constructionism) is important because it becomes the educational foundation of the virtual course, with a view to achieving a sustainable development (Thorne & Macgregor, 2018), with bases in collaborative learning (Rodriguez, Hudson, & Niblock, 2018) and using learning objects that have "smart" characteristics (Ahn et al, 2018) to make the student's learning process easier. 5.…”

Section: Theoretical Foundation Of the Operational Indicators Of The mentioning

confidence: 99%

Operational Indicators of the Learning Management System in Virtual Spaces Supported by ICT

Capacho¹,

Jimeno²,

Salazar³

2019

Turkish Online Journal of Distance Education

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The paper presents the conceptual framework and operational indicators of the Learning Management System in virtual spaces supported by ICT. The framework of the management system has three layers that are operational, management-operative, and management-decision. The conceptual foundation of the system is related to the Science of Education, the Science of Computing and the Science of Administration, and takes into account the representative model of the teaching-learning process of the student in virtual spaces supported by ICT. The dimensions, characteristics, variables, and indicators of the management system are assembled on the model in question. The indicators presented in the article correspond to 153 indicators of the operational layer. These were evaluated internationally with a confidence level of more than 90%. The operative indicators are contained in the dimensions of identification, academic, pedagogical-didactic, formative, of assessment of the virtual learning, and technological of the system of management of the virtual learning. The importance of the indicators presented ensures the success of the operational phase of the management system. Therefore, once the operation of the system is guaranteed, the success of the management-operative and management-decision layers of the system is assured. The correct fulfillment of the layers above guarantees the achievement of both the student's learning in the virtual course and the institution that offers virtual teaching through virtual learning platforms supported by ICT.

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Section: Theoretical Foundation Of the Operational Indicators Of The mentioning

confidence: 99%

Operational Indicators of the Learning Management System in Virtual Spaces Supported by ICT

Capacho¹,

Jimeno²,

Salazar³

2019

Turkish Online Journal of Distance Education

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“…With the support of booming infrastructure, many such training programs are now adopting AI-driven augmented reality (AR)/virtual reality (VR) lesson plans. Interconnected modules driven by computer vision, NLU, NLP, text-to-speech (TTS), information retrieval algorithms facilitate lessons and/or assessments in biology (Ahn et al, 2018), surgery and medicine (Mirchi et al, 2020), pathological laboratory analysis (Taoum et al, 2016), and military leadership training (Gordon et al, 2004).…”

Section: Reactive Engagement Of Ai For Education Tutoring Aidsmentioning

confidence: 99%

Proactive and reactive engagement of artificial intelligence methods for education: a review

Mallik

Gangopadhyay

2023

Front. Artif. Intell.

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The education sector has benefited enormously through integrating digital technology driven tools and platforms. In recent years, artificial intelligence based methods are being considered as the next generation of technology that can enhance the experience of education for students, teachers, and administrative staff alike. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled these efforts further. In this review article, we investigate how artificial intelligence, machine learning, and deep learning methods are being utilized to support the education process. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety—from students admissions, course scheduling, and content generation in the proactive planning phase to knowledge delivery, performance assessment, and outcome prediction in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 195 original research articles published in the past two decades, i.e., 2003–2022. We discuss the paradigm shifts in the solution approaches proposed, particularly with respect to the choice of data and algorithms used over this time. We further discuss how the COVID-19 pandemic influenced this field of active development and the existing infrastructural challenges and ethical concerns pertaining to global adoption of artificial intelligence for education.

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“…These systems have been developed to directly help learners and also instructors choose useful and relevant learning resources such as courses or exercises based on deep reinforcement learning [5]. Some systems have also been developed to meet educational requirements among the massive number of educational resources [6]. Moreover, recommender systems useful for technology enhanced learning may support and enhance learning practices in design, development and test of socio-technical innovations [7].…”

Section: Related Workmentioning

confidence: 99%

A Compositional Adaptation-based Approach for Recommending Learning Resources in Software Development

Mahmoudi¹,

Badie

Moosaee

et al. 2022

IJE

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In this paper, we discussed the application of a compositional adaptation approach to recommend learning resources to users in the area of software development. This approach makes use of a domainspecific ontology in this area to find those words, which are used in the technical description of the stored cases. A point peculiar with representing cases in the proposed approach is to take into account the characteristics of included learning resources, which justify the way they support the essential operations in the case of solution. In this way, only those components that comply with user's request would be considered in the final solution. In the paper, the performance of the proposed approach for recommending learning resources together with the status of user experience in his/ her interaction with the resulted recommending system, have been evaluated. Results demonstrate the fact that the learning resources through this approach are sufficiently beneficial for the users. Although the proposed approach has been applied for recommending learning resources in the area of software development, it can be equally applied to any technological area through developing domain-specific ontology for that area. This is mainly because any technological area has its own specific objects/ entities holding their own semantic similarities that finally lead to forming a domain-specific ontology for that area.

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Intelligent Virtual Reality Tutoring System Supporting Open Educational Resource Access

Cited by 6 publications

References 11 publications

Operational Indicators of the Learning Management System in Virtual Spaces Supported by ICT

Operational Indicators of the Learning Management System in Virtual Spaces Supported by ICT

Proactive and reactive engagement of artificial intelligence methods for education: a review

A Compositional Adaptation-based Approach for Recommending Learning Resources in Software Development

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