Design Of An Efficient Model For Enhancing Online Teaching Platform Adoption Among Teachers During Pandemics DOI Open Access

Shubham Sachdeva

Published: May 1, 2024

In the rapidly evolving educational landscape, necessitated by unprecedented challenges of pandemic, imperative need to adopt effective online teaching modules has become paramount. Existing methods in assessing and enhancing integration technology education have revealed significant limitations, particularly their failure accurately gauge address multifaceted faced educators. These include a lack comprehensive analysis technical pedagogical obstacles, insufficient consideration social influences impacting teachers' attitudes, disregard for facilitating conditions crucial adoption learning platforms. To bridge this gap, study introduces an innovative approach, employing Graph Neural Networks combined with Grey Wolf Coot Optimizer (GWCO), enhance efficiency classification process. This methodology is uniquely positioned dissect understand intricate web factors influencing behavioral intentions attitudes towards during pandemic scenarios. The proposed model leverages synergistic effect assessment estimate which, when influence, predicts intention sets. intention, further analyzed alongside conditions, provides robust understanding rates superiority approach evidenced its performance on multiple real-time datasets. It demonstrated 8.5% increase precision, 3.9% higher accuracy, 8.3% boost recall, 4.9% AUC (Area Under Curve), 4.5% rise specificity, 1.9% reduction delay compared existing methodologies. advancements not only signify substantial improvement over current models but also mark stride platforms educators face pandemic-induced challenges. work, thus, stands at forefront research, offering invaluable insights practical solutions adoption. paves way more nuanced, efficient, education, aligning dynamic needs system times crisis. implications research are far-reaching, providing foundational framework future studies applications realm especially scenarios demanding rapid adaptation digital

Language: Английский

Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction DOI
Irfan Ali, Nosheen Fatima Warraich,

Khadijah Butt

et al.

Information Development, Journal Year: 2024, Volume and Issue: unknown

Published: June 5, 2024

The aim of present study was to measure the relationship UTAUT (Unified Theory Acceptance and Use Technology) TAM (Technology Model) variables regarding AI technology AI-based applications acceptance in education sector. Research carried out by using PRISMA (Preferred reporting items for systematic review meta-analysis) guidelines. relevant studies were searched from major databases that included a) Scopus, b) Web Science. Initial search retrieved 309 titles, 30 articles conference papers selected following process. Data analysed CMA (Comprehensive Meta-analysis) Meta-Essential software. Findings exhibit between BI accept high (PE → BI), medium (EE BI, SI low (FC BI). magnitude constructs remained all paths (PU AT, PEOU PU Theoretically, this meta-analysis provided a panoramic picture two leading models acceptance/adoption This way forward researchers extend research on including ChatGPT, intelligent tutoring, robots, Chatbots, voice assistants. Practically, findings are useful IT companies, decision makers educational institutes designing implementing applications.

Language: Английский

Citations

3
The Effect of Emotional Intelligence on Higher Education: A Pilot Study on the interplay Between Artificial Intelligence, Emotional Intelligence, and E-Learning DOI Creative Commons
Abdullah Alenezi

Multidisciplinary Journal for Education Social and Technological Sciences, Journal Year: 2024, Volume and Issue: 11(2), P. 51 - 77

Published: Oct. 8, 2024

Integrating Artificial Intelligence (AI) and E-learning platforms has become increasingly prevalent in the rapidly evolving landscape of higher Education. However, amidst this technological advancement, role Emotional (EI) its impact on efficacy AI-driven educational tools still needs to be explored. This pilot study seeks elucidate intricate relationship between Intelligence, E-Learning Higher Drawing upon a multidisciplinary approach, investigates correlation students' competencies their engagement with platforms. The findings are expected shed light several critical aspects. Firstly, it aims uncover how influences receptivity AI-infused environments, potentially elucidating strategies for optimizing user experience learning outcomes. Moreover, by exploring reciprocal influence AI algorithms, research endeavors contribute refinement technologies, fostering greater personalization adaptability settings. Furthermore, address ethical implications inherent intersection E-Learning. By potential risks benefits associated integrating these inform policymakers, educators, developers alike, facilitating responsible deployment tools. Therefore, innovative methodology comprehensive approach aspire pave way future endeavors, ultimately enriching insights prioritizing advancement human well-being.

Language: Английский

Citations

2
College Teachers’ Behavioral Intention to Adopt Artificial Intelligence-Assisted Teaching Systems DOI Creative Commons

Wenwen Zhang,

Zhaofeng Hou

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 152812 - 152824

Published: Jan. 1, 2024

Language: Английский

Citations

1
Exploring the Predictors of AI Chatbot Usage Intensity Among Students: Within- and Between-Person Relationships Using the Technology Acceptance Model DOI Creative Commons
Anne‐Kathrin Kleine, Insa Schaffernak, Eva Lermer

et al.

Computers in Human Behavior Artificial Humans, Journal Year: 2024, Volume and Issue: unknown, P. 100113 - 100113

Published: Dec. 1, 2024

Language: Английский

Citations

1
Design Of An Efficient Model For Enhancing Online Teaching Platform Adoption Among Teachers During Pandemics DOI Open Access

Shubham Sachdeva

Published: May 1, 2024

In the rapidly evolving educational landscape, necessitated by unprecedented challenges of pandemic, imperative need to adopt effective online teaching modules has become paramount. Existing methods in assessing and enhancing integration technology education have revealed significant limitations, particularly their failure accurately gauge address multifaceted faced educators. These include a lack comprehensive analysis technical pedagogical obstacles, insufficient consideration social influences impacting teachers' attitudes, disregard for facilitating conditions crucial adoption learning platforms. To bridge this gap, study introduces an innovative approach, employing Graph Neural Networks combined with Grey Wolf Coot Optimizer (GWCO), enhance efficiency classification process. This methodology is uniquely positioned dissect understand intricate web factors influencing behavioral intentions attitudes towards during pandemic scenarios. The proposed model leverages synergistic effect assessment estimate which, when influence, predicts intention sets. intention, further analyzed alongside conditions, provides robust understanding rates superiority approach evidenced its performance on multiple real-time datasets. It demonstrated 8.5% increase precision, 3.9% higher accuracy, 8.3% boost recall, 4.9% AUC (Area Under Curve), 4.5% rise specificity, 1.9% reduction delay compared existing methodologies. advancements not only signify substantial improvement over current models but also mark stride platforms educators face pandemic-induced challenges. work, thus, stands at forefront research, offering invaluable insights practical solutions adoption. paves way more nuanced, efficient, education, aligning dynamic needs system times crisis. implications research are far-reaching, providing foundational framework future studies applications realm especially scenarios demanding rapid adaptation digital

Language: Английский

Citations

0