A Smart Testing Model Based on Mining Semantic Relations

Qaffas, Alaa A.; Idrees, Amira M.; Khedr, Ayman E.; Kholeif, Sherif

doi:10.1109/access.2023.3260406

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…They have a significant impact on how students learn and interact with course materials [43][44][45][46]. Several models, such as the VARK model (Visual, Auditory, Reading/Writing, and Kinesthetic) and Kolb's Learning Style Inventory, have been developed to classify different approaches to learning [47]. Teachers may better cater to their students' individual learning styles by keeping these models in mind while they develop lesson plans and course content.…”

Section: Overview Of the Iot And E-learning In Heismentioning

confidence: 99%

IoT Adoption Model for E-Learning in Higher Education Institutes: A Case Study in Saudi Arabia

Ali,

Madni,

Jahangeer

et al. 2023

Sustainability

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The realm of the Internet of Things (IoT), while continually transforming as a novel paradigm in the nexus of technology and education, still contends with numerous obstacles that hinder its incorporation into higher education institutions’ (HEIs) e-learning platforms. Despite substantial strides in IoT utilization from industrialized nations—the United States, the United Kingdom, Japan, and China serving as prime exemplars—the scope of its implementation in developing countries, notably Saudi Arabia, Malaysia, Pakistan, and Bangladesh, lags behind. A significant gap exists in research centered on the trajectory of IoT integration within e-learning systems of economically disadvantaged nations. Specifically, this study centers on Saudi Arabia to illuminate the main factors catalyzing or encumbering IoT uptake within its HEIs’ e-learning sector. As a preliminary step, this research has embarked on an exhaustive dissection of prior studies to unearth critical variables implicated in the IoT adoption process. Subsequently, we employed an inferential methodology, amassing data from 384 respondents in Saudi Arabian HEIs. Our examination divulges that usability, accessibility, technical support, and individual proficiencies considerably contribute to the rate of IoT incorporation. Furthermore, our data infer that financial obstacles, self-efficacy, interactive capability, online surveillance, automated attendance tracking, training programs, network and data safeguarding measures, and relevant tools significantly influence IoT adoption. Contrarily, factors such as accessibility, internet quality, infrastructure preparedness, usability, privacy concerns, and faculty support appeared to have a negligible impact on the adoption rates within HEIs. This research culminates in offering concrete recommendations to bolster IoT integration within Saudi Arabian HEIs, presenting valuable insights for government entities, policy architects, and HEIs to address the hurdles associated with IoT implementation in the higher education sector.

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