Move to Smart Learning Environment: Exploratory Research of Challenges in Computer Laboratory and Design Intelligent Virtual Laboratory for eLearning Technology

Munawar, Saima; Toor, Saba Khalil; Aslam, Muhammad; Hamid, Muhammad

doi:10.29333/ejmste/85036

Cited by 31 publications

(17 citation statements)

References 21 publications

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“…Personalization, i.e., supporting personalized features, ensures smart classrooms remain convenient, healthy, and safe (Zheng et al, 2019;Choi & Suk, 2016;Vasanthapriyan & Randima, 2019;Sevindik, 2010;Lee, 2015;Uzelac et al, 2015;Ouf et al, 2016). Challenges also arise in personalizing the learning environment to promote learning attitudes and motivation (Munawar et al, 2018;Tlili et al, 2019;Aguilar et al, 2017). Success of smart classrooms also depends on personalization of teaching methods by educators (Miraoui, 2018;Godlewska et al, 2019).…”

Section: Smart Classrooms In Education: a Backgroundmentioning

confidence: 99%

Exploring quality attributes of smart classrooms from the perspectives of academics

2023

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The purpose of this study is to explore attributes that influence the quality of smart classrooms from the perspective of higher education teachers. Relying on a purposive sample of 31 academicians from Gulf Cooperation Council (GCC) countries, the study identifies themes related to quality attributes of technology platforms and social interactions. These attributes are user security, educational intelligence, technology accessibility, system diversity, system interconnectivity, system simplicity, system sensitivity, system adaptability and platform affordability. The study identifies management procedures, educational policies, and administrative practices that enact, engineer, enable, and enhance these attributes in smart classrooms. The findings also highlight strategy-oriented planning and cause-driven transformation as the main smart classroom contexts influencing the quality of education among interviewees. With insights from the interviews, this article discusses some theoretical and practical implications of the study, research limitations, and potential future research directions.

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Section: Smart Classrooms In Education: a Backgroundmentioning

confidence: 99%

Exploring quality attributes of smart classrooms from the perspectives of academics

2023

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“…e situation requires intelligent algorithms to provide feedback automatically. For example, with the help of pedagogical agent-based cognitive architecture, the intelligent virtual laboratory was developed to give appropriate feedback to students who encounter difficulties in the laboratory [35]. Besides, a learning website, Jutge.org, was developed with the features of a rich and well-organized problem repository.…”

Section: Feedback One Of the Challenges Impairing Personalized Learning Is The Inappropriate Sequencing Of Contentsmentioning

confidence: 99%

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

et al. 2021

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This study provided a content analysis of studies aiming to disclose how artificial intelligence (AI) has been applied to the education sector and explore the potential research trends and challenges of AI in education. A total of 100 papers including 63 empirical papers (74 studies) and 37 analytic papers were selected from the education and educational research category of Social Sciences Citation Index database from 2010 to 2020. The content analysis showed that the research questions could be classified into development layer (classification, matching, recommendation, and deep learning), application layer (feedback, reasoning, and adaptive learning), and integration layer (affection computing, role-playing, immersive learning, and gamification). Moreover, four research trends, including Internet of Things, swarm intelligence, deep learning, and neuroscience, as well as an assessment of AI in education, were suggested for further investigation. However, we also proposed the challenges in education may be caused by AI with regard to inappropriate use of AI techniques, changing roles of teachers and students, as well as social and ethical issues. The results provide insights into an overview of the AI used for education domain, which helps to strengthen the theoretical foundation of AI in education and provides a promising channel for educators and AI engineers to carry out further collaborative research.

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“…Ghergulescu et al [13] introduced an interactive scientific virtual laboratory architecture, enabling students to create experiments and to practice at their own pace. With the help of an artificial intelligence paradigm, Munawar et al [14] proposed an intelligent virtual laboratory using a pedagogical agent-based cognitive architecture.…”

Section: A Virtual Teachingmentioning

confidence: 99%

A Design of Smart Beaker Structure and Interaction Paradigm Based on Multimodal Fusion Understanding

et al. 2020

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Virtual chemistry experiment is an important teaching tool in middle schools. It can not only help students understand the experimental principles, but also help them memorize the experimental procedures. However, there are still some problems in virtual chemistry experiments. First, in existing research, user intentions are often misunderstood and cannot be accurately understood. Second, the existing research fail to identify the user's wrong actions, which reduce the accuracy of the experiment. Third, the user sense of operation and realism is not strong during the experiment, which reduces the user's experience. Finally, the lack of navigation guidance for experimental operations increases user learning time. In order to solve these problems. This paper proposes a scheme for establishing an intelligent navigational chemical laboratory based on multimodal fusion. First of all, we design a new smart beaker structure with perceptual ability, which can be used to complete most chemical experiments and give users a real sense of experience. Besides, we propose a multimodal fusion understanding algorithm, which reduces the misidentification of the experiment and better understands the real intention of the user. Finally, intelligent navigation and wrong behavior recognition functions are added to the experimental equipment, which improves the efficiency of human-computer interaction. The results show that Compared with the existing virtual laboratory or system, the chemical laboratory scheme proposed in this paper through multimodal fusion understanding algorithm greatly reduces the user's memory load and improves the success rate of the experiment. Moreover, through the combination of virtual and real, the virtual chemistry experiment not only improves the authenticity of the operation, but also stimulates the students' interest in learning, which is well received by users.

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Move to Smart Learning Environment: Exploratory Research of Challenges in Computer Laboratory and Design Intelligent Virtual Laboratory for eLearning Technology

Cited by 31 publications

References 21 publications

Exploring quality attributes of smart classrooms from the perspectives of academics

Exploring quality attributes of smart classrooms from the perspectives of academics

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

A Design of Smart Beaker Structure and Interaction Paradigm Based on Multimodal Fusion Understanding

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