Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools

Alam, Ashraf; Mohanty, Atasi

doi:10.1080/23311916.2023.2283282

Cited by 23 publications

(8 citation statements)

References 215 publications

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“…In the ever-evolving landscape of education, the fusion of Educational Technology (EdTech) with Artificial Intelligence (AI) has emerged as a dynamic force, transforming traditional teaching methodologies and redefining the learning experience. This paper delves into the various dimensions of educational technology enhanced by AI, providing an overview of its impact on curriculum development, content creation, assessment methods, virtual reality, interactive technologies, and administrative processes (Aithal & Maiya, 2023, Alam & Mohanty, 2023, Saklani, 2023.…”

Section: Educational Technology Enhanced By Aimentioning

confidence: 99%

AI in education: A review of personalized learning and educational technology

Oyebola Olusola Ayeni,

Nancy Mohd Al Hamad,

Onyebuchi Nneamaka Chisom

et al. 2024

GSC Adv. Res. Rev.

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The integration of Artificial Intelligence (AI) in education has ushered in a transformative era, redefining traditional teaching and learning methods. This review explores the multifaceted role of AI in education, with a particular focus on personalized learning and educational technology. The synergy between AI and education promises to address individualized needs, enhance student engagement, and optimize learning outcomes. Personalized learning, enabled by AI algorithms, tailors educational experiences to the unique needs, preferences, and pace of each student. This approach goes beyond a one-size-fits-all model, fostering a more inclusive and effective learning environment. The review delves into the diverse applications of AI-driven personalized learning, ranging from adaptive content delivery and real-time feedback to intelligent tutoring systems. It analyzes the impact of these technologies on student performance, highlighting the potential to narrow educational gaps and cater to diverse learning styles. Educational technology, powered by AI, extends beyond the classroom, encompassing online platforms, virtual reality, and interactive tools. The review explores the integration of AI in curriculum development, content creation, and assessment methods, offering insights into how these technologies augment the teaching and learning experience. Furthermore, the review examines the role of AI in automating administrative tasks, allowing educators to redirect their focus towards personalized instruction. Challenges and ethical considerations associated with the adoption of AI in education are also scrutinized. Privacy concerns, algorithmic biases, and the digital divide are discussed, emphasizing the importance of responsible AI implementation. The review underscores the need for collaborative efforts among educators, policymakers, and technologists to establish ethical guidelines and ensure the equitable distribution of AI-enhanced educational resources. This review provides a comprehensive examination of the evolving landscape of AI in education, with a spotlight on personalized learning and educational technology. As the symbiosis between AI and education continues to evolve, this synthesis of current research and trends aims to guide future developments, fostering an informed and progressive approach to the integration of AI in the educational sphere.

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Section: Educational Technology Enhanced By Aimentioning

confidence: 99%

AI in education: A review of personalized learning and educational technology

Oyebola Olusola Ayeni,

Nancy Mohd Al Hamad,

Onyebuchi Nneamaka Chisom

et al. 2024

GSC Adv. Res. Rev.

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“…The first aspect of the conceptual framework focuses on students' active participation in various online activities. These activities include searching for information, social networking, online payment, gaming, and other relevant digital interactions (Alam & Mohanty, 2023). This variable is deemed independent as it is hypothesized to influence both internet addiction and academic performance.…”

Section: Excessive Internet Use and Academic Achievementmentioning

confidence: 99%

“…We posit that to fully understand the causes and effects of online activity engagement, it is crucial to consider various variables such as engagement as related to the individual or the situation, internet addiction as a mediating variable, academic performance as the dependent variable, and demographic characteristics as moderating variables (Dinc, 2022;Chen & Wang, 2023;Chawla & Joshi, 2018). The first aspect of our conceptual framework centers on students' active participation in diverse online activities, encompassing behaviors such as searching for information, social networking, online payment, gaming, and other relevant digital interactions (Alam & Mohanty, 2023). This framework provides a theoretical foundation for investigating the intricate connections between students' engagement in online activities, internet addiction, and academic performance among university students in China, serving as a roadmap for our study's design, data collection, and analysis (Fig.…”

Section: Excessive Internet Use and Academic Achievementmentioning

confidence: 99%

From clicks to credits: examining the influence of online engagement and internet addiction on academic performance in Chinese universities

Jiang,

Joshi,

Khanal

2024

Int J Educ Technol High Educ

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University students’ engagement in online activities can impact both internet addiction and academic performance. This research aims to investigate these effects among university students. Using data from the "China College Students Longitudinal Survey" conducted by the Chinese Academy of Social Sciences between 2013 and 2018, this study examines the influence of university students’ engagement in online activities. With a sample size of 15,102 university students, we explored the relationship between online activity, internet addiction, and academic performance. Employing stratified random sampling, the study investigates sample characteristics, including gender, study level, time spent online, and experiences accessing websites. Academic performance is assessed through six categories, employing descriptive and inferential statistics such as t-tests, ANOVA, stepwise linear regression, and path analysis. The findings indicate significantly high levels of university students’ engagement in online activities, accompanied by a high prevalence of internet addiction. Key factors determining university students’ engagement, internet addiction, and academic performance include gender, study level, time spent online, and experiences accessing websites beyond the Great Firewall. The study establishes a complex relationship, revealing negative effects of specific online engagements on internet addiction, while the latter influences university students’ engagement. Overall, this research contributes valuable insights into the intricate interplay between online behaviors, addiction, and academic performance among Chinese university students, with implications for educational policies and interventions.

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“…In the contemporary landscape of rapid technological progress, integrating state-of-the-art technologies has become intrinsic to augmenting the efficiency and responsiveness of critical systems [1]. Within this realm of innovations, iRESPOND stands out as a real-time Geospatial Information and Alert System, assuming a pioneering role in technology to facilitate timely and informed decision-making in dynamic and unpredictable environments.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

Prianes,

Fortuno,

Onesa

et al. 2024

IJACSA

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In the era of rapid technological advancement, the integration of cutting-edge technologies plays a pivotal role in enhancing the efficiency and responsiveness of critical systems. iRESPOND, a real-time Geospatial Information and Alert System, stands at the forefront of such innovations, facilitating timely and informed decision-making in dynamic environments. As the demand for accurate and swift responses, the role of CNN models in iRESPOND becomes significant. The study focuses on seven prominent CNN architectures, namely EfficientNet (B0, B7, V2B0, and V2L), InceptionV3, ResNet50, and VGG19 and with the integration of different optimizers and learning rates. The methodology employed a strategic implementation of looping during the training phase. This iterative approach is designed to systematically re-train the CNN models, emphasizing identifying the most suitable architecture among the seven considered variants. The primary objective is to discern the optimal architecture and fine-tune critical parameters, explicitly targeting the optimizer and learning rate values. The differential impact of each model on the system's ability is to discern patterns and anomalies in the image datasets. ResNet50 exhibited robust performance showcasing suitability for real-time processing in dynamic environments with a better accuracy result of 95.02%. However, the EfficientNetV2B0 model, characterized by its advancements in network scaling, presented promising results with a lower loss of 0.187. Generally, the findings not only contribute valuable insights into the optimal selection of architectures for iRESPOND but also highlight the importance of fine-tuning hyperparameters through an iterative training approach, which paves the way for the continued enhancement of iRESPOND as an adaptive system.

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Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools

Cited by 23 publications

References 215 publications

AI in education: A review of personalized learning and educational technology

AI in education: A review of personalized learning and educational technology

From clicks to credits: examining the influence of online engagement and internet addiction on academic performance in Chinese universities

Exploring the Landscape: Analysis of Model Results on Various Convolutional Neural Network Architectures for iRESPOND System

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