Investigating Students' Satisfaction with Online Collaborative Learning During the COVID-19 Period: An Expectation-Confirmation Model.

The recent outbreak of COVID-19 posed discontinuous disruption to traditional learning modes worldwide. In order to keep social distance, online collaborative learning has become a necessity during the pandemic. However, our understanding of students' well-being and satisfaction with online collaborative learning is limited, especially during the COVID-19 period. Leveraging expectation confirmation theory, this study focuses on the triggers and inhibitors of students' cognitive load during online collaborative learning process and their subsequent satisfaction with the learning mode during the pandemic. We used a mixed-method approach in this study. We conducted a qualitative study with interview data and a quantitative study with surveys. The results indicate several psychological and cognitive antecedents of students' cognitive load during online collaborative learning. Findings also indicate that a high level of cognitive load will decrease students' perceived usefulness of the online learning platform and expectation confirmation, thus leading to a low level of satisfaction with online collaborative learning. This study can provide theoretical and practical implications for a better understanding of online student groups' satisfaction with online collaborative learning during the COVID-19 period.

Exploring information dissemination effect on social media: an empirical investigation.

Operators make profits by publishing information. If they can know the influencing factors in the process of information dissemination, they can provide new insights for practical operations and formulate corresponding operation strategies for different types of accounts. The purpose of this article is to discuss the information dissemination process of WeChat public accounts and what factors will affect the reading rate and sharing rate of the article. In this paper, the "feedback-sympathize-identify participant-share" (FSIPS) two-stage model is used to analyze the characteristics of information dissemination, and the negative binomial regression model is used to analyze which factors have a significant impact on the two stages of the dissemination model. Our data is obtained from a company that specializes in operating WeChat Official Accounts, and the data is authentic and more valuable. We collectively consider the roles of users, message content, interactions between content and individuals, and the context of social media in information dissemination behaviors (i.e., reading and sharing). In this process, some new variables, such as environment-related variables, are involved and incorporated into the two stages of information dissemination for analysis. The results show that the like rate, which is one of the feedback dimensions, has the greatest impact on the reading rate, while the favorite rate has the greatest impact on the sharing rate. Article type also plays a crucial role in the dissemination of information. In addition, the content relevance between the title and the content also largely affects the share rates of the three types.

Understanding Users' Group Behavioral Decisions About Sharing Articles in Social Media: An Elaboration Likelihood Model Perspective.

The decision to share information is a common phenomenon in individuals' daily social media use (e.g., Twitter, micro-blogs). However, research on the information to be shared mainly focuses on short texts, and the research on long texts/article sharing is relatively limited. Based on the elaboration likelihood model (ELM), this study established a conceptual model to reveal the determinants of users' behavior in sharing articles. Data on 1311 articles were collected on WeChat, China's most popular social media, and were processed using multiple linear regression. We found that both the central path and the peripheral path of the ELM affect users' decision-making about article-sharing behavior, and that amount of reading and perceived usefulness have the greatest impact. The rhetorical title, the number of pictures, and the number of fans have a negative impact on users' decision-making about article-sharing behavior. Further, the factors that affect users' online-community sharing and sharing with friends are also different. This study is one of the first to apply ELM to examine the influencing factors of users' decisions about sharing general articles on social media, contributing to the research on the decision-making behavior of users sharing long texts on social media.

An investigation on trust in AI-enabled collaboration: Application of AI-Driven chatbot in accommodation-based sharing economy.

Several measures taken to control the spread of the COVID-19 pandemic have severely disrupted the accommodation sharing sector. This study attempts to find solutions to aid the recovery of the accommodation sharing sector via team efforts. Accordingly, we focus on the integration of artificial intelligence (AI) and collaboration. Despite the significant developments in AI technologies, there exists no research considering the application of AI in team collaboration. Utilizing the design science research method and collaboration engineering, we developed an AI-driven prototype system, AI-Driven, for collaboration process recommendation. Qualitative results show that the newly developed tool for collaboration process recommendation has achieved satisfactory performance. Furthermore, we investigated the antecedents and outcomes of trust in the AI-driven collaboration context. From a practical perspective, we propose several solutions to the challenges looming over the accommodation sharing sector according to collaboration deliverables. Furthermore, a system prototype was developed to facilitate collaboration process recommendation and provide procedural guidance.

Conductive and capacitive network for enriching the exoelectrogens and enhancing the extracellular electron transfer in microbial fuel cells.

Although lots of nanomaterials modified anodes have been reported to improve the bacterial attachment and extracellular electron transfer (EET) in microbial fuel cells (MFCs), the lack of a three dimensional (3D) conductive and capacitive network severely limited MFCs performance. In this work, 3D conductive networks derived from mucor mycelia were grown on carbon cloth (CC), and capacitive FeMn phosphides/oxides were further anchored on these 3D networks by electrochemical deposition (denoted as FeMn/CMM@CC) to simultaneously address the above challenges. As a result, the multivalent metal active sites were evenly distributed on 3D conductive network, which favored the enrichment of exoelectrogens, mass transport and EET. Consequently, the as-prepared FeMn/CMM@CC anode displayed accumulated charge of 131.4C/m2, higher than bare CC. Meanwhile, FeMn/CMM@CC anode substantially promoted flavin excretion and the amounts of nano conduits. The abundance of Geobacter was 63 % on bare CC, and greatly increased to 83 % on FeMn/CMM@CC. MFCs equipped by FeMn/CMM@CC anode presented the power density of 3.06 W/m2 and coulombic efficiency (29.9 %), evidently higher than bare CC (1.29 W/m2, 7.3 %), and the daily chemical oxygen demand (COD) removal amount also increased to 92.6 mg/L/d. This work developed a facile method to optimize the abiotic-biotic interface by introducing 3D conductive and capacitive network, which was proved to be a promising strategy to modify macro-porous electrodes.

Enhanced microorganism attachment and flavin excretion in microbial fuel cells via an N,S-codoped carbon microflower anode.

Commonly used dense arrays of nanomaterials on carbon cloth (CC) are not suitable to accommodate microorganisms in microbial fuel cells (MFCs) due to their unmatched size. To simultaneously enrich exoelectrogens and accelerate the extracellular electron transfer (EET) process, SnS2 nanosheets were selected as sacrificial templates to prepare binder-free N,S-codoped carbon microflowers (N,S-CMF@CC) by polymer coating and pyrolysis. N,S-CMF@CC showed a cumulative total charge of 125.70C/m2, approximately 2.11 times higher than that of CC, indicating its better electricity storage capacity. Moreover, the interface transfer resistance and diffusion coefficient in bioanodes were 42.68 Ω and 9.27 × 10-10 cm2/s, respectively, superior to CC (141.3 Ω and 1.06 × 10-11 cm2/s). Remarkably, N,S-codoped carbon microflowers excreted more flavin than CC, as confirmed by continuous fluorescence monitoring. Biofilm and 16S rRNA gene sequence analysis revealed that exoelectrogens were enriched, and nanoconduits were generated on the N,S-CMF@CC anode. In particular, flavin excretion was also promoted on our hierarchical electrode, effectively driving the EET process. MFCs equipped with the N,S-CMF@CC anode could deliver a power density of 2.50 W/m2, coulombic efficiency of 22.77 %, and chemical oxygen demand (COD) removal amount of 90.72 mg/L/d, higher than that of bare CC. These findings not only demonstrate that our anode is capable of solving the cell enrichment issue, but it may also increase EET rates by bound flavin with outer membrane c-type cytochromes (OMCs) to simultaneously boost the power generation and wastewater treatment performance of MFCs.

3D Hierarchical Co8FeS8-FeCo2O4/N-CNTs@CF with an Enhanced Microorganisms-Anode Interface for Improving Microbial Fuel Cell Performance.

Microbial fuel cells (MFCs) are promising ecofriendly techniques for harvesting bioenergy from organic and inorganic matter. Currently, it is challenging to design MFC anodes with favorable microorganism attachment and fast extracellular electron transfer (EET) rate for high MFC performance. Here we prepared N-doped carbon nanotubes (NCNTs) on carbon felt (CF) and used it as a support for growing hierarchical Co8FeS8-FeCo2O4/NCNTs core-shell nanostructures (FeCo/NCNTs@CF). We observed improved wettability, specific areal capacitance, and diffusion coefficient, as well as small charge transfer resistance compared with bare CF. MFCs equipped with FeCo/NCNTs@CF displayed a power density of 3.04 W/m2 and COD removal amount of 221.0 mg/L/d, about 47.6 and 290.1% improvements compared with that of CF. Biofilm morphology and 16s rRNA gene sequence analysis proved that our anode facilitated the enrichment growth of exoelectrogens. Flavin secretion was also promoted on our hierarchical elelctrode, effectively driving the EET process. This work disclosed that hierarchical nanomaterials modified electrode with tailored physicochemical properties is a promising platform to simultaneously enhance exoelectrogen attachment and EET efficiency for MFCs.