E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

Immune response induced by novel coronavirus infection.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2 has been prominent around the world since it was first discovered, affecting more than 100 million people. Although the symptoms of most infected patients are not serious, there is still a considerable proportion of patients who need hospitalization and even develop fatal symptoms such as cytokine storms, acute respiratory distress syndrome and so on. Cytokine storm is usually described as a collection of clinical manifestations caused by overactivation of the immune system, which plays an important role in tissue injury and multiorgan failure. The immune system of healthy individuals is composed of two interrelated parts, the innate immune system and the adaptive immune system. Innate immunity is the body's first line of defense against viruses; it can quickly perceive viruses through pattern recognition receptors and activate related inflammatory pathways to clear pathogens. The adaptive immune system is activated by specific antigens and is mainly composed of CD4+ T cells, CD8+ T cells and B cells, which play different roles in viral infection. Here, we discuss the immune response after SARS-CoV-2 infection. In-depth study of the recognition of and response of innate immunity and adaptive immunity to SARS-CoV-2 will help to prevent the development of critical cases and aid the exploration of more targeted treatments.

High-throughput screening of spike variants uncovers the key residues that alter the affinity and antigenicity of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has elicited a worldwide pandemic since late 2019. There has been ~675 million confirmed coronavirus disease 2019 (COVID-19) cases, leading to more than 6.8 million deaths as of March 1, 2023. Five SARS-CoV-2 variants of concern (VOCs) were tracked as they emerged and were subsequently characterized. However, it is still difficult to predict the next dominant variant due to the rapid evolution of its spike (S) glycoprotein, which affects the binding activity between cellular receptor angiotensin-converting enzyme 2 (ACE2) and blocks the presenting epitope from humoral monoclonal antibody (mAb) recognition. Here, we established a robust mammalian cell-surface-display platform to study the interactions of S-ACE2 and S-mAb on a large scale. A lentivirus library of S variants was generated via in silico chip synthesis followed by site-directed saturation mutagenesis, after which the enriched candidates were acquired through single-cell fluorescence sorting and analyzed by third-generation DNA sequencing technologies. The mutational landscape provides a blueprint for understanding the key residues of the S protein binding affinity to ACE2 and mAb evasion. It was found that S205F, Y453F, Q493A, Q493M, Q498H, Q498Y, N501F, and N501T showed a 3-12-fold increase in infectivity, of which Y453F, Q493A, and Q498Y exhibited at least a 10-fold resistance to mAbs REGN10933, LY-CoV555, and REGN10987, respectively. These methods for mammalian cells may assist in the precise control of SARS-CoV-2 in the future.

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights.

Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data's variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms.

Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests.

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.

Oral Fungal Alterations in Patients with COVID-19 and Recovered Patients.

The oral bacteriome, gut bacteriome, and gut mycobiome are associated with coronavirus disease 2019 (COVID-19). However, the oral fungal microbiota in COVID-19 remains unclear. This article aims to characterize the oral mycobiome in COVID-19 and recovered patients. Tongue coating specimens of 71 COVID-19 patients, 36 suspected cases (SCs), 22 recovered COVID-19 patients, 36 SCs who recovered, and 132 controls from Henan are collected and analyzed using internal transcribed spacer sequencing. The richness of oral fungi is increased in COVID-19 versus controls, and beta diversity analysis reveals separate fungal communities for COVID-19 and control. The ratio of Ascomycota and Basidiomycota is higher in COVID-19, and the opportunistic pathogens, including the genera Candida, Saccharomyces, and Simplicillium, are increased in COVID-19. The classifier based on two fungal biomarkers is constructed and can distinguish COVID-19 patients from controls in the training, testing, and independent cohorts. Importantly, the classifier successfully diagnoses SCs with positive specific severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies as COVID-19 patients. The correlation between distinct fungi and bacteria in COVID-19 and control groups is depicted. These data suggest that the oral mycobiome may play a role in COVID-19.

Longitudinal association between problematic smartphone use and sleep disorder among Chinese college students during the COVID-19 pandemic.

BACKGROUND: Problematic smartphone use (PSU) and sleep disorders (SD) are common public health problems among college students. While previous cross-sectional studies have found a relationship between PSU and SD, the causal direction of this relationship remains unclear. This study aims to examine the longitudinal changes of PSU and SD during the COVID-19 pandemic, determine the causal relationship between them, and identify confounding factors that affect this association. METHODS: The study sample consisted of 1186 Chinese college students (47.7% male) with a mean age of 18.08 years. Participants completed the Smartphone Addiction Scale - Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI) at both baseline and follow-up surveys, conducted one year apart. The cross-lagged panel model (CLPM) was used to examine the causal relationship between PSU and SD, stratified by gender and duration of daily physical activity. The fixed effect panel regression was used to confirm the findings of CLPM. RESULTS: The results of the CLPM analysis showed a significant bidirectional relationship between PSU and SD for the overall sample, which was consistent with the fixed effects model findings. However, subgroup analyses revealed that the bidirectional association disappeared among males or those who engaged in daily physical activity for more than 1 h. CONCLUSIONS: Our study shows a significant bidirectional association between PSU and SD, with variations across gender and daily physical activity levels. Encouraging physical activity may serve as a potential intervention to disrupt the bidirectional association between PSU and SD, which has important implications for public health strategies aimed at reducing the negative consequences of PSU and SD.

Using an influenza surveillance system to estimate the number of SARS-CoV-2 infections in Beijing, China, weeks 2 to 6 2023.

With COVID-19 public health control measures downgraded in China in January 2023, reported COVID-19 case numbers may underestimate the true numbers after the SARS-CoV-2 Omicron wave. Using a multiplier model based on our influenza surveillance system, we estimated that the overall incidence of SARS-CoV-2 infections was 392/100,000 population in Beijing during the 5 weeks following policy adjustment. No notable change occurred after the Spring Festival in early February. The multiplier model provides an opportunity for assessing the actual COVID-19 situation.

HLA-Bw4 in association with KIR3DL1 favors natural killer cell-mediated protection against severe COVID-19.

Replicating SARS-CoV-2 has been shown to degrade HLA class I on target cells to evade the cytotoxic T-cell (CTL) response. HLA-I downregulation can be sensed by NK cells to unleash killer cell immunoglobulin-like receptor (KIR)-mediated self-inhibition by the cognate HLA-I ligands. Here, we investigated the impact of HLA and KIR genotypes and HLA-KIR combinations on COVID-19 outcome. We found that the peptide affinities of HLA alleles were not correlated with COVID-19 severity. The predicted poor binders for SARS-CoV-2 peptides belong to HLA-B subtypes that encode KIR ligands, including Bw4 and C1 (introduced by B*46:01), which have a small F pocket and cannot accommodate SARS-CoV-2 CTL epitopes. However, HLA-Bw4 weak binders were beneficial for COVID-19 outcome, and individuals lacking the HLA-Bw4 motif were at higher risk for serious illness from COVID-19. The presence of the HLA-Bw4 and KIR3DL1 combination had a 58.8% lower risk of developing severe COVID-19 (OR = 0.412, 95% CI = 0.187-0.904, p = 0.02). This suggests that HLA-Bw4 alleles that impair their ability to load SARS-CoV-2 peptides will become targets for NK-mediated destruction. Thus, we proposed that the synergistic responsiveness of CTLs and NK cells can efficiently control SARS-CoV-2 infection and replication, and NK-cell-mediated anti-SARS-CoV-2 immune responses being mostly involved in severe infection when the level of ORF8 is high enough to degrade HLA-I. The HLA-Bw4/KIR3DL1 genotype may be particularly important for East Asians undergoing COVID-19 who are enriched in HLA-Bw4-inhibitory KIR interactions and carry a high frequency of HLA-Bw4 alleles that bind poorly to coronavirus peptides.

COVID-19 vaccine uptake, hesitancy and clinical effects on patients with Takayasu's arteritis: A web-based questionnaire survey from a large cohort.

Objective: This study aimed to investigate the Coronavirus disease 2019 (COVID-19) vaccination rate, reasons for vaccine hesitancy and clinical effects on patients with Takayasu's arteritis (TAK). Methods: A web-based survey was administered to a TAK cohort established by the Department of Rheumatology, Zhongshan Hospital through WeChat in April, 2022. Responses from a total of 302 patients were received. The Sinovac or Sinopharm inactivated vaccination rate, side effects, and vaccine hesitancy reasons were analyzed. In addition, disease flare, new disease onset, and changes of immune-related parameters after vaccination were analyzed in vaccinated patients. Results: Among 302 patients, 93 (30.79%) received the inactivated COVID-19 vaccination. Among the 209 unvaccinated patients, the most common reason for hesitancy were concern about side effects (136, 65.07%). Vaccinated patients had a longer disease duration (p = 0.08) and lower use of biologic agents (p < 0.001); 16 (17.20%) of the 93 vaccinated patients developed side effects, and most of them were mild; 8 (8.60%) developed disease flares or new-onset disease 12-128 days post-vaccination and 2 (2.15%) developed serious adverse effects (vision defect and cranial infarction). Immune-related parameters of 17 patients indicated decreases in IgA and IgM after vaccination (p < 0.05). Eighteen (19.35%) of the 93 vaccinated patients were diagnosed post-vaccination.These patients had a significantly higher percentage of CD19+ B cells at disease onset (p < 0.05) than the unvaccinated patients diagnosed at the same time. Conclusion: The vaccination rate was low in TAK, which was mainly caused by concerns about negative effects of vaccination on their disease. An acceptable safety profile was observed in vaccinated patients. The risk of disease flare associated with COVID-19 vaccination warrants further investigation.

Porcine Enteric Alphacoronavirus Entry through Multiple Pathways (Caveolae, Clathrin, and Macropinocytosis) Requires Rab GTPases for Endosomal Transport.

Porcine enteric alphacoronavirus (PEAV) is a new bat HKU2-like porcine coronavirus, and its endemic outbreak has caused severe economic losses to the pig industry. Its broad cellular tropism suggests a potential risk of cross-species transmission. A limited understanding of PEAV entry mechanisms may hinder a rapid response to potential outbreaks. This study analyzed PEAV entry events using chemical inhibitors, RNA interference, and dominant-negative mutants. PEAV entry into Vero cells depended on three endocytic pathways: caveolae, clathrin, and macropinocytosis. Endocytosis requires dynamin, cholesterol, and a low pH. Rab5, Rab7, and Rab9 GTPases (but not Rab11) regulate PEAV endocytosis. PEAV particles colocalize with EEA1, Rab5, Rab7, Rab9, and Lamp-1, suggesting that PEAV translocates into early endosomes after internalization, and Rab5, Rab7, and Rab9 regulate trafficking to lysosomes before viral genome release. PEAV enters porcine intestinal cells (IPI-2I) through the same endocytic pathway, suggesting that PEAV may enter various cells through multiple endocytic pathways. This study provides new insights into the PEAV life cycle. IMPORTANCE Emerging and reemerging coronaviruses cause severe human and animal epidemics worldwide. PEAV is the first bat-like coronavirus to cause infection in domestic animals. However, the PEAV entry mechanism into host cells remains unknown. This study demonstrates that PEAV enters into Vero or IPI-2I cells through caveola/clathrin-mediated endocytosis and macropinocytosis, which does not require a specific receptor. Subsequently, Rab5, Rab7, and Rab9 regulate PEAV trafficking from early endosomes to lysosomes, which is pH dependent. The results advance our understanding of the disease and help to develop potential new drug targets against PEAV.

Comparison of mental health symptoms before and during the covid-19 pandemic: evidence from a systematic review and meta-analysis of 134 cohorts.

OBJECTIVE: To synthesise results of mental health outcomes in cohorts before and during the covid-19 pandemic. DESIGN: Systematic review. DATA SOURCES: Medline, PsycINFO, CINAHL, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, medRxiv, and Open Science Framework Preprints. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies comparing general mental health, anxiety symptoms, or depression symptoms assessed from 1 January 2020 or later with outcomes collected from 1 January 2018 to 31 December 2019 in any population, and comprising ≥90% of the same participants before and during the covid-19 pandemic or using statistical methods to account for missing data. Restricted maximum likelihood random effects meta-analyses (worse covid-19 outcomes representing positive change) were performed. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist for Prevalence Studies. RESULTS: As of 11 April 2022, 94 411 unique titles and abstracts including 137 unique studies from 134 cohorts were reviewed. Most of the studies were from high income (n=105, 77%) or upper middle income (n=28, 20%) countries. Among general population studies, no changes were found for general mental health (standardised mean difference (SMD)change 0.11, 95% confidence interval -0.00 to 0.22) or anxiety symptoms (0.05, -0.04 to 0.13), but depression symptoms worsened minimally (0.12, 0.01 to 0.24). Among women or female participants, general mental health (0.22, 0.08 to 0.35), anxiety symptoms (0.20, 0.12 to 0.29), and depression symptoms (0.22, 0.05 to 0.40) worsened by minimal to small amounts. In 27 other analyses across outcome domains among subgroups other than women or female participants, five analyses suggested that symptoms worsened by minimal or small amounts, and two suggested minimal or small improvements. No other subgroup experienced changes across all outcome domains. In three studies with data from March to April 2020 and late 2020, symptoms were unchanged from pre-covid-19 levels at both assessments or increased initially then returned to pre-covid-19 levels. Substantial heterogeneity and risk of bias were present across analyses. CONCLUSIONS: High risk of bias in many studies and substantial heterogeneity suggest caution in interpreting results. Nonetheless, most symptom change estimates for general mental health, anxiety symptoms, and depression symptoms were close to zero and not statistically significant, and significant changes were of minimal to small magnitudes. Small negative changes occurred for women or female participants in all domains. The authors will update the results of this systematic review as more evidence accrues, with study results posted online (https://www.depressd.ca/covid-19-mental-health). REVIEW REGISTRATION: PROSPERO CRD42020179703.

Temporal dynamics of SARS-CoV-2 genome mutations that occurred in vivo on an aircraft.

We analyzed variations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome during a flight-related cluster outbreak of coronavirus disease 2019 (COVID-19) in Shenzhen, China, to explore the characteristics of SARS-CoV-2 transmission and intra-host single nucleotide variations (iSNVs) in a confined space. Thirty-three patients with COVID-19 were sampled, and 14 were resampled 3-31 days later. All 47 nasopharyngeal swabs were deep sequenced. iSNVs and similarities in the consensus genome sequence were analyzed. Three SARS-CoV-2 variants of concern, Delta (n=31), Beta (n=1), and C.1.2 (n=1), were detected among the 33 patients. The viral genome sequences from 30 Delta-positive patients had similar SNVs; 14 of these patients provided two successive samples. Overall, the 47 sequenced genomes contained 164 iSNVs. Of the 14 paired (successive) samples, the second samples (T2) contained more iSNVs (median: 3; 95% confidence interval [95%CI]: 2.77-10.22) than did the first samples (T1; median: 2; 95%CI: 1.63-3.74; Wilcoxon test, P=0.021). 38 iSNVs were detected in T1 samples, and only seven were also detectable in T2 samples. Notably, T2 samples from two of the 14 paired samples had additional mutations than the T1 samples. The iSNVs of the SARS-CoV-2 genome exhibited rapid dynamic changes during a flight-related cluster outbreak event. Intra-host diversity increased gradually with time, and new site mutations occurred in vivo without a population transmission bottleneck. Therefore, we could not determine the generational relationship from the mutation site changes alone.

Characterization of viral shedding of Severe acute respiratory syndrome Coronavirus-2

Corona virus disease 2019 (COVID-19) is an emerging pandemic of highly contagious caused by severe acute respiratory syndrome coronavirus -2 (SARS-CoV-2). Understanding the infectivity of various clinical samples and its transmission routes have been the main focus of current researches since the causative pathogens was identified. In this comprehensive review, we discuss the viral shedding from different clinical samples and reveal that infectious virus may be mainly discharged through respiratory and digestive systems. Also, SARS-CoV-2 showed a potential tropism for eyes, kidney, testis, placenta and other extrapulmonary tissues and high viral loads correlated with severe conditions. A better understanding of viral shedding may help the studies on pathogenesis and transmission of SARS-CoV-2 and provide suggestions for the disease control.

A triple-RBD-based mucosal vaccine provides broad protection against SARS-CoV-2 variants of concern.

The rapid mutation and spread of SARS-CoV-2 variants urge the development of effective mucosal vaccines to provide broad-spectrum protection against the initial infection and thereby curb the transmission potential. Here, we designed a chimeric triple-RBD immunogen, 3Ro-NC, harboring one Delta RBD and two Omicron RBDs within a novel protein scaffold. 3Ro-NC elicits potent and broad RBD-specific neutralizing immunity against SARS-CoV-2 variants of concern. Notably, intranasal immunization with 3Ro-NC plus the mucosal adjuvant KFD (3Ro-NC + KFDi.n) elicits coordinated mucosal IgA and higher neutralizing antibody specificity (closer antigenic distance) against the Omicron variant. In Omicron-challenged human ACE2 transgenic mice, 3Ro-NC + KFDi.n immunization significantly reduces the tissue pathology in the lung and lowers the viral RNA copy numbers in both the lung (85.7-fold) and the nasal turbinate (13.6-fold). Nasal virologic control is highly correlated with RBD-specific secretory IgA antibodies. Our data show that 3Ro-NC plus KFD is a promising mucosal vaccine candidate for protection against SARS-CoV-2 Omicron infection, pathology and transmission potential.

IgM, IgG and IgA antibody responses to SARS-CoV-2 infection in patients with coronavirus disease 2019

Objective: To understand the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) specific IgM, IgC and IgA responses in corona Virus disease 2019(COVID-19) patients. Method: The SARS-CoV-2-specific IgM and IgG levels were determined by the enzyme-linked immunosorbent assay while IgA 1evels were detected by the chemiluminescence immune detection systems.

Interruption of Influenza Transmission under Public Health Emergency Response for COVID-19: a Study Based on Real-World Data from Beijing, China.

To estimate the effect of the corona virus disease 2019 (COVID-19) control measures taken to mitigate community transmission in many regions, we analyzed data from the influenza surveillance system in Beijing from week 27 of 2014 to week 26 of 2020. We collected weekly numbers of influenza-like illness (ILI) cases, weekly positive proportion of ILI cases, weekly ILI case proportion in outpatients, and the dates of implementation of COVID-19 measures. We compared the influenza activity indicators of the 2019/2020 season with the preceding five seasons and built two ARIMAX models to estimate the effectiveness of COVID-19 measures declared since January 24, 2020 by the emergency response. Based on the observed data, compared to the preceding five influenza seasons, ILIs, positive proportion of ILIs, and duration of the influenza epidemic period in 2019/2020 had increased from 13% to 54%; in particular, the number of weeks from the peak to the end of the influenza epidemic period had decreased from 12 to 1. According to ARIMAX model forecasting, after considering natural decline, weekly ILIs had decreased by 48.6%, weekly positive proportion had dropped by 15% in the second week after the emergency response was declared, and COVID-19 measures had reduced by 83%. We conclude that the public health emergency response can significantly interrupt the transmission of influenza.

Influencing factors of virus clearance of COVID-19 patients

Objective: To analysis the related factors that influenced the elimination of virus in patients with coronavirus disease 2019 (COVID-19), and help with its prevention and control.

Influences of Online Learning Environment on International Students' Intrinsic Motivation and Engagement in the Chinese Learning

With the impact of the COVID-19 pandemic, Chinese teaching for international students in Chinese universities has largely moved online. Despite the comprehensive literature regarding the influences of environmental factors on domestic students' learning in traditional learning environment, few studies have addressed the influences of online learning environment (OLE) on international students' Chinese learning experiences. We focus on international students in intensive Chinese courses at a Chinese university, and explores the influences of OLE on these students' intrinsic motivation (IM) towards and engagement in Chinese learning during the COVID-19 pandemic. Data were collected from an online questionnaire survey and follow-up interviews. The results revealed that the participants had positive perceptions of the online Chinese learning environment, and that the participants had high levels of IM towards and engagement in their Chinese learning. The results also showed the positive impact of the participants' perceived OLE on their IM towards and engagement in Chinese learning. The research, though with several limitations, has implications for teachers teaching Chinese as a foreign language and institutions promoting international students' IM and SE in online teaching contexts.

Research on the influencing factors of consumers’ green purchase behavior in the post-pandemic era

The severe impact of the COVID-19 pandemic on the world has caused consumers to think about environmental issues. Although green products are very important to environmental sustainability, the factors that influence consumers' purchase behavior of green products is unclear. This study aims to explore the impact mechanism of social media marketing on consumers' green product purchase behavior in the post-pandemic era. Based on the theory of planned behavior (TPB), this study constructs an extended TPB model to understand the influencing factors of Chinese consumers' green product purchase behavior in the post-pandemic era. The empirical results of 489 questionnaires show that social media marketing, product knowledge and crisis awareness have a direct or indirect positive influence on purchase intentions. Perceived behavioral control and intentions have positive effects on behavior. The attribution of responsibility facilitates the relationship between intentions and behaviors. The results have important implications for enterprises’ sustainability strategies and provide a framework for investigating green buying behaviors in Chinese culture in the post-pandemic era.