AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform.

Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student's learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.

The Impact of COVID-19 on Physical Activity Among Adults in Saudi Arabia: A Cross-Sectional Study.

Background Physical inactivity has been identified as a major factor in developing and progressing chronic non-communicable diseases such as obesity. The Kingdom of Saudi Arabia ranks high worldwide in rates of obesity. During the coronavirus disease 2019 (COVID-19) pandemic, public health measures have been enforced. These included social distancing, masking, reduction of workplace daily hours, prevention of social gatherings, and home quarantine measures. These ultimately restricted the ability to perform regular physical health activities. The aim of this study is to understand the impact of COVID-19 on physical activity among adults in the Kingdom of Saudi Arabia. Methodology A cross-sectional study was conducted among the Saudi population. An online survey was sent through social media to gather data regarding individual physical activity before and after the start of the COVID-19 restrictions. The data were collected from March 20, 2021, until May 20, 2021, and analyzed using chi-square and paired t-test using the SAS software version 9.4. Results In total, 433 participants completed the survey. There were 183 (42.3%) males, and the majority of the participants were Saudi nationals (284, 65.6%). Most of the participants (181, 41.8%) were in the age group 25-35 years and 253 (58%) had bachelor's degrees. Although the results did not show a statistically significant difference between pre- and post-COVID-19 respondents in terms of physical activity, married participants, participants from the eastern province, and participants who did not exercise regularly were all significantly impacted by lack of exercise compared to their counterparts (p < 0.05). Conclusions Taking measures to prevent the spread of COVID-19 is essential. Nonetheless, recommendations should be sought for physical activity during lockdowns, and large-scale research should be conducted to better understand what causes the exaggeration of sedentary lifestyles during lockdowns and how to prevent them. Further studies need to be conducted, and national guidelines should be made available in case of a future lockdown.

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments.

BACKGROUND: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. METHODS: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. RESULT AND CONCLUSIONS: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.

Assessing Student Perceptions of a Norwegian University's COVID-19 Response Strategy: A Cross-Sectional Study.

Aim: This study aims to investigate Norwegian students' perceptions toward a higher education institution (HEI)'s COVID-19 response strategy, differentiating between three behavioral techniques: informing (i. e., email updates about COVID-19), nudging (i.e., visual cues as reminders), and creating novel opportunities (i.e., provision of antibacterial dispensers). In addition, the study assesses to what extent these perceptions are influenced by COVID-19 related psychological factors: risk perception; attitudes toward infection prevention and control (IPC) behaviors; perceived behavior control; institutional trust. Methods: A cross-sectional online survey was conducted among a student population. The survey was developed to evaluate the HEI's response strategy, and distinct perceptions of COVID-19 and related practices. Structural equation modeling (SEM) was applied to estimate the effect of the psychological factors on the attitude toward different behavioral techniques. Results: Creating novel opportunities was perceived most positively from the students, secondly, informing the students through email updates about COVID-19, finally, reminders through visual cues. Institutional trust presented the largest positive effect on informing the students through email updates, while no effect was measured for reminders. Attitudes toward IPC behaviors showed the strongest effect on students' perceptions of new opportunities and reminders, whereas providing email updates about COVID-19 is less affected by pre-existing perceptions. Conclusions: A host of factors such as institutional trust, and perceptions concerning IPC measures and risk severity, influence students' perceptions of different behavior change techniques. This type of knowledge can contribute to understanding how perceptions can impact acceptance and adoption of specific preventive measures within a pandemic response. An assessment as such may result in more ethical and relevant future efforts.

Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm was trained by Term Frequency Inverse Document Frequency (TF-IDF) bigram features, which contribute a network training model. The algorithm was tested on two different real-world datasets from the CBC news network and COVID-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97–99%. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents, which may contribute negatively to the COVID-19 pandemic.

Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm.

Great efforts are now underway to control the coronavirus 2019 disease (COVID-19). Millions of people are medically examined, and their data keep piling up awaiting classification. The data are typically both incomplete and heterogeneous which hampers classical classification algorithms. Some researchers have recently modified the popular KNN algorithm as a solution, where they handle incompleteness by imputation and heterogeneity by converting categorical data into numbers. In this article, we introduce a novel KNN variant (KNNV) algorithm that provides better results as demonstrated by thorough experimental work. We employ rough set theoretic techniques to handle both incompleteness and heterogeneity, as well as to find an ideal value for K. The KNNV algorithm takes an incomplete, heterogeneous dataset, containing medical records of people, and identifies those cases with COVID-19. We use in the process two popular distance metrics, Euclidean and Mahalanobis, in an effort to widen the operational scope. The KNNV algorithm is implemented and tested on a real dataset from the Italian Society of Medical and Interventional Radiology. The experimental results show that it can efficiently and accurately classify COVID-19 cases. It is also compared to three KNN derivatives. The comparison results show that it greatly outperforms all its competitors in terms of four metrics: precision, recall, accuracy, and F-Score. The algorithm given in this article can be easily applied to classify other diseases. Moreover, its methodology can be further extended to do general classification tasks outside the medical field.

The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation.

BACKGROUND: Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments. OBJECTIVE: The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. METHODS: To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called "diversity." A diversity score for a given drug was calculated by measuring how "diverse" a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. RESULTS: From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride. CONCLUSIONS: The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.