Student Engagement and Academic Achievement as Precursors to Knowledge Management: Dynamics of Post COVID Offline Classroom Student Engagement and Achievement

Purpose: Conclusive evidence from Pre-Covid research has exhibited a moderately positive correlation between Students' Engagement with Academic Achievement(AA) as significant precursors of student-centred learning (Hao Lei et al., 2018). Gunuc and Kuzu (2014) defined Student Engagement (SE) in terms of psychological, emotional, cognitive and behavioural reactions to the students' learning process. However, the dynamics aligned to these constructs remains ambiguous for want of research on Behavioural, Cognitive & Emotional engagement of students in Post Covid offline classroom of higher educational institutions (HEIs). This study measured Student Engagement basis the three sub-constructs, its impact on Academic Achievement, and its subsequent impact on Knowledge Management, given the fact that there is a perception that post covid offline classrooms have suffered on the mentioned accounts. Design/Methodology/Approach: A structured questionnaire catering to constructs of components of Student Engagement viz Emotional, Cognitive, Behavioural Engagement, Academic Achievement & Knowledge Management was floated amongst the target population, and Structural equation modelling evaluated the inter-relationship dynamics between the constructs. Findings: Structural Equation Modelling (SEM) was applied after confirmatory analysis. Examination of Path coefficients revealed that Emotional Engagement, Behavioural Engagement and Cognitive Engagement have significant relationships with Academic Achievement. The results also conveyed that Academic Achievement relates to Knowledge Management conclusively in offline classroom settings Post Covid. Originality/Value: Pedagogical research & teaching-learning outcomes in research mandate the significance of Student Engagement & the subsequent effect on Academic Achievement and Knowledge Management in HEIs. This study reinforced the relevance of this equation and its applicability in Post Covid offline classrooms in HEIs of North India.. 300 university & college students were a part of this study to evaluate the Post Covid learning paradigm, as offline classes took over. © 2022,Journal of Content, Community and Communication. All Rights Reserved.

A Spotlight on Novel Coronavirus (COVID - 19) with Various Types of Strains

Deadly COVID-19 viruses have raised a pandemic situation in the year 2019, causing serious and contagious respiratory infections in humans. SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the main causative agent for this disease outbreak. The pandemic created a critical impact on the global economy. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis that lasted about 11 months. Since, late 2020, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations. This resulted so far, in over 2.7 million deaths and near about 122 million infection cases. Most mutations in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome are either deleterious and swiftly purged or relatively neutral. As far as the concern is the variants it impacts the virus characteristics, including antigenicity and transmissibility in response to the modification of the human immune profile. In recent days, COVID-19 affected cases are rapidly increasing and it became difficult to inhibit this virus as they are continuously mutated in the host cell forming various new strains like B.1.1.7, B.1.351, P.1, P.2, B.1.1.529, etc. These monitoring, surveillance of variation, and sequencing efforts within the SARS-CoV-2 genome enabled the rapid identification of the first some of Variants of Concern (VOCs) in late 2020, where genome changes became the most observable impact on virus biology and disease transmission. In this review article, we tried to focus and spot the light on the genetic diversification of various strains, their nature, similarities and dissimilarities, mechanism of action, and the prophylactic interventions which could prevent this life-threatening disease in the long run.

Evaluation of predictive machine learning models for drug repurposing against delta variant of SARS-CoV-2 spike protein

Drug repurposing is a major approach used by researchers to tackle the COVID-19 pandemic which has been worsened by the current surge of delta variant in many countries. Though drugs like Remdesivir and Hydroxychloroquine have been repurposed, studies prove these drugs have insignificant effect in treatment. So, in this study, we use the already FDA approved database of 1615 drugs to apply semi-flexible and flexible molecular docking methods to calculate the docking scores and identify the best 20 potential inhibitors for our modelled delta variant spike protein RBD. Then, we calculate 2325 1-D and 2-D molecular descriptors and use machine-learning algorithms like K-Nearest Neighbor, Random Forest, Support Vector Machine and ensemble stacking method to build regression-based prediction models. We identify 15 best descriptors for the dataset all of which were found to be inversely correlated with ligand binding. With only these few descriptors, the models performed excellently with an area under curve (AUC) value of 0.952 in Regression Error Characteristic curve for ensemble stacking. Therefore, we comment that these 15 descriptors are the most important features for the binding of inhibitors to the spike protein and hence these should be studied properly in terms of drug repurposing and drug discovery. © 2022, National Institute of Science Communication and Policy Research. All rights reserved.