Hybrid Reality-Based Education Expansion System for Non-Traditional Learning

Khan, Firoz; Kumar, Randhir; kadry, seifedine

doi:10.26555/jiteki.v7i1.20568

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…The detailed healthcare application was developed by [23] which use Blockchain and Business Process Management in Health Care, Especially for COVID-19 Cases. The idea of using Hybrid Reality-Based Education Expansion System for Non-Traditional Learning in [24]. Trust-Aware Routing Framework for Internet of Things was discussed in [25].…”

Section: Methodsmentioning

confidence: 99%

Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring

Ramasamy

Khan

Shah

et al. 2022

Sensors

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The functionality of the Internet is continually changing from the Internet of Computers (IoC) to the “Internet of Things (IoT)”. Most connected systems, called Cyber-Physical Systems (CPS), are formed from the integration of numerous features such as humans and the physical environment, smart objects, and embedded devices and infrastructure. There are a few critical problems, such as security risks and ethical issues that could affect the IoT and CPS. When every piece of data and device is connected and obtainable on the network, hackers can obtain it and utilise it for different scams. In medical healthcare IoT-CPS, everyday medical and physical data of a patient may be gathered through wearable sensors. This paper proposes an AI-enabled IoT-CPS which doctors can utilise to discover diseases in patients based on AI. AI was created to find a few disorders such as Diabetes, Heart disease and Gait disturbances. Each disease has various symptoms among patients or elderly. Dataset is retrieved from the Kaggle repository to execute AI-enabled IoT-CPS technology. For the classification, AI-enabled IoT-CPS Algorithm is used to discover diseases. The experimental results demonstrate that compared with existing algorithms, the proposed AI-enabled IoT-CPS algorithm detects patient diseases and fall events in elderly more efficiently in terms of Accuracy, Precision, Recall and F-measure.

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Section: Methodsmentioning

confidence: 99%

Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring

Ramasamy

Khan

Shah

et al. 2022

Sensors

Self Cite

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“…Third, MR, also known as Hybrid Reality, combines the virtual world and the real world to implement new information, such as new environments or visualisations, which complements the shortcomings of VR and AR to realise a more evolved virtual world (Khan et al ., 2021; Viglialoro et al ., 2021). The key difference from AR is that MR reacts with real objects (e.g.…”

Section: Four Realities Of Metaversementioning

confidence: 99%

Metaverse‐Based Learning Through Children's School Space Design

Dreamson¹,

Park²

2023

Int J Art Design Ed

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Metaverse is understood with technological terms such as augmented reality, lifelogging, mirrored worlds, and virtual worlds, and using a metaverse platform is considered an emerging form of learning. Yet, its pedagogical features, such as self-learning, collaborative learning, and learning-by-doing, are identical to those of online learning. In this study, we argue that metaverse-based learning refers to a new reality learners create by assuming that the existence of reality is different in the metaverse. We extracted six educational values in epistemological and ontological senses, reviewing the four realities of the metaverse, including virtual reality, augmented reality, mixed reality, and extended reality, which is a way to avoid technological determinists'; approach to education. Based on the valuesbottom-up, collaboration, authorship, ownership, interconnectivity, and community, primary school children's (year 5 & 6) groups (n = 20) redesigned 20 assigned school spaces such as classroom teacher office, science room, and music room on their chosen metaverse platforms.Throughout the thematic analysis of design artifacts, activity scenarios, and reflection & presentation, we discovered four themes: collaborative learning with co-ownership and coauthorship; being interconnected with all living and non-living things; co-participants with different roles; and transdisciplinary research-driven learning, which is used to articulate a framework for metaverse-based learning, which is different from online learning.

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“…Selection for the best SVM hyperparameter values is done by applying natural optimizing techniques [29]. (iii) for nontraditional learning, expansion of hybrid reality-based education is done [30].…”

Section: Related Workmentioning

confidence: 99%

Quest_SA: Preprocessing Method for Closed-Ended Questionnaires Using Sentiment Analysis through Polarity

M¹,

Shanthi

2022

Mobile Information Systems

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Sentiment analysis is a prominent research topic in natural language processing, with applications in politics, news, education, product review, and other sectors. Especially in the education sector, sentiment analysis can assist educators in finding students’ feelings about a course on time, altering the teaching plan appropriately and timely to improve the quality of education and teaching. For students, the sentiment analysis can identify emotions, academic performance, behaviour, and so on; the primary purpose of this research paper is to analyze students’ emotions, self-esteem, and efficacy based on closed-ended questionnaires. This paper proposes Quest_SA, which uses the sentiment analysis technique to identify students’ emotions based on the answer provided by a closed-ended questionnaire. The polarity value is assigned for each questionnaire scale. The students’ responses are then gathered using a closed-ended questionnaire, and the student’s emotions are classified using a polarity-based method of sentiment analysis. Finally, sentiment scores and emotion variance were used to evaluate the outcomes. According to the sentiment ratings, students have favourable sentiments and emotions such as unhappy, somewhat happy, and happy. The real-world closed-ended questionnaires such as emotional intelligence, Eysenck, personality, self-determination scale, self-efficacy, Rosenberg’s self-esteem, positive and negative affect schedule, and Oxford happiness questionnaires were used to examine the academic performance with the proposed sentiment analysis. This study inferred that the proposed sentiment analysis preprocessing method with polarity scores is as accurate as the standard value calculation.

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Hybrid Reality-Based Education Expansion System for Non-Traditional Learning

Cited by 3 publications

References 25 publications

Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring

Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring

Metaverse‐Based Learning Through Children's School Space Design

Quest_SA: Preprocessing Method for Closed-Ended Questionnaires Using Sentiment Analysis through Polarity

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