Meta-Analysis of EMF-Induced Pollution by COVID-19 in Virtual Teaching and Learning With an Artificial Intelligence Perspective

Das, Swapna Sikha; Srivastava, Shilpa; Tripathi, Anshuman

doi:10.4018/ijwltt.285566

Cited by 3 publications

(1 citation statement)

References 59 publications

(49 reference statements)

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“…Treve (2021) has analyzed the challenges in higher education, and Aboagye, Yawson, and Appiah (2021) focused their research on challenges faced by students. Scholars emphasized the negative effects during covid-impacted virtual teaching and learning (Das, Srivastava, Tripathi, & Das, 2022) as well as psychological and motivational aspects (Abdimusa, Kuatbekov, Ismailova, Shchedrina, & Kulanina, 2022). As far as education (as area of interest), two relevant face-to-face surveys are presented below.…”

Section: Introductionmentioning

confidence: 99%

Online technologies in teaching and learning. Lessons learnt while teaching during COVID-19 pandemic in Romania: towards a “dual” education system

2023

Advances in Education and Educational Trends Series

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The educationas processes and systems, teachers and studentswas affected by corona-virus pandemic, across the world. Since pandemic imposed rigorous social distancing, the education process has migrated to online environment, supported by appropriate technologies, with multi-sided effects on students, teachers, and technology producers. Thus, amid its profound negative impact, corona-virus pandemic functioned as an accelerator of using new online teaching technologies. The authors' scope of work encompassed a variety of education levels (from elementary to higher education) in Romanian educational environment. This chapter aims at summarizing major lessons learned by the authors' direct experiences of teaching under pandemic in two different education settings (international school, university)with the general objective to formulate recommendations to: (i) teachers; (ii) students; (iii) education policymakers; as well as specific objectives: to identify similarities and significant differences among students by age and level of education; and eventually formulate recommendations for technology producers. Essentially qualitative, the research methodology included secondary research (literature survey) and primary research methods (observation, interview and survey)based on the authors' direct experience, yet teaching both local and international students. This study contributes at filling a literature gap, and opening further research paths in the field of online education.

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Section: Introductionmentioning

confidence: 99%

Online technologies in teaching and learning. Lessons learnt while teaching during COVID-19 pandemic in Romania: towards a “dual” education system

2023

Advances in Education and Educational Trends Series

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Multi-criteria decision making (MCDM) in diverse domains of education: a comprehensive bibliometric analysis for research directions

Srivastava,

Tripathi,

Arora

2024

Int J Syst Assur Eng Manag

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An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders

Vandana,

Srivastava,

Arora

et al. 2024

Int. Res. J. multidiscip. Technovation

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Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indicating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model.

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Meta-Analysis of EMF-Induced Pollution by COVID-19 in Virtual Teaching and Learning With an Artificial Intelligence Perspective

Cited by 3 publications

References 59 publications

Online technologies in teaching and learning. Lessons learnt while teaching during COVID-19 pandemic in Romania: towards a “dual” education system

Online technologies in teaching and learning. Lessons learnt while teaching during COVID-19 pandemic in Romania: towards a “dual” education system

Multi-criteria decision making (MCDM) in diverse domains of education: a comprehensive bibliometric analysis for research directions

An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders

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