Search

Abstract The availability of large-scale learning data presents unprecedented opportunities for investigating student learning processes. However, it is challenging for instructors to fully make sense of this data and effectively support their teaching practices. This study introduces LearningViz, an interactive learning analytics dashboard to help instructors identify, analyze, and close performance gaps among students in their classes. In this dashboard, we incorporated three modules to enhance human and computer interactions for better supporting the teaching practices: the Student Overall Performance Analysis Module, which provides a comprehensive understanding of students’ learning in the course; the Student Group Performance Analysis Module, which examines performance gaps across different groups and identifies factors contributing to these gaps; and the Final Exam Item Analysis Module, which evaluates the quality of exam questions and identifies strategies for closing performance gaps. The overall design of the platform follows a user-centered approach, integrating data analysis with various visualization strategies in a unified platform. A case study is then conducted to highlight the effectiveness of LearningViz in supporting instructors analyzing students’ learning patterns and associated factors impacting learning performance. We further conduct a usability test with several domain experts, to evaluate the usefulness and effectiveness of this platform in supporting the teaching practices. Our findings underscore the platform's ability to support instructors in detecting performance gaps among students, investigating influential factors, evaluating assessment quality and implementing targeted instructional strategies for closing performance gaps.

The current study explores the use of computer vision and artificial intelligence (AI) methods for analyzing 360-degree spherical video-based virtual reality (SVVR) data. The study aimed to explore the potential of AI, computer vision, and machine learning methods (including entropy analysis, Markov chain analysis, and sequential pattern mining), in extracting salient information from SVVR video data. The research questions focused on differences and distinguishing characteristics of autistic and neurotypical usage characteristics in terms of behavior sequences, object associations, and common patterns, and the extent to which the predictability and variability of findings might distinguish the two participant groups and provide provisional insights into the dynamics of their usage behaviors. Findings from entropy analysis suggest the neurotypical group showed greater homogeneity and predictability, and the autistic group displayed significant heterogeneity and variability in behavior. Results from the Markov Chains analysis revealed distinct engagement patterns, with autistic participants exhibiting a wide range of transition probabilities, suggesting varied SVVR engagement strategies, and with the neurotypical group demonstrating more predictable behaviors. Sequential pattern mining results indicated that the autistic group engaged with a broader spectrum of classes within the SVVR environment, hinting at their attraction to a diverse set of stimuli. This research provides a preliminary foundation for future studies in this area, as well as practical implications for designing effective SVVR learning interventions for autistic individuals.

Recent experimental studies have led to the suggestion that short-range correlations may be a major contributor to the nuclear EMC effect. This hypothesis requires that the structure function for nucleons involved in short-range correlations should be heavily suppressed compared to that of a free nucleon. Based on calculations performed within an AdS/QCD motivated, light-front quark-diquark model, we find that this large suppression of the nucleon structure function leads to a strong suppression of the nucleon elastic form factors.

Abstract Knowledge Building has been advanced as a pedagogy of engaged learning where students identify as a community whose purpose is to advance their shared ideas. This approach, which has been studied for three decades (Scardamalia & Bereiter, in: K. Sawyer (ed) Cambridge handbook of the learning sciences, Cambridge University Press, 2014), includes cognitive, social constructivist, and emotional elements (Zhu et al. in User Modeling and User-Adapted Interaction, 29: 789–820, 2019b). This paper investigates how refining Knowledge Building activities based on students’ feedback impacts their social, cognitive, and emotional engagement. Using a design-based research method, we refined successive course activities based on feedback from 23 Masters of Education students. With successive iterations, we found that the density of students’ reading networks increased; they theorized more deeply, introduced more authoritative resources, and made greater efforts to integrate ideas within the community knowledge base. As well, their level of negative affect decreased. These findings suggest that soliciting students’ input into course design can benefit their engagement and disposition toward learning, with implications for curriculum design.

Both Gram-negative and Gram-positive bacteria can release vesicle-like structures referred to as bacterial extracellular vesicles (BEVs), which contain various bioactive compounds. BEVs play important roles in the microbial community interactions and host-microbe interactions. Markedly, BEVs can be delivered to host cells, thus modulating the development and function of the innate immune system. To clarify the compositions and biological functions of BEVs, we need to collect these vesicles with high purity and bioactivity. Here we propose an isolation strategy based on a broad-spectrum antimicrobial epsilon-poly-L-lysine (ϵ-PL) to precipitate BEVs at a relatively low centrifugal speed (10,000 × g). Compared to the standard ultracentrifugation strategy, our method can enrich BEVs from large volumes of media inexpensively and rapidly. The precipitated BEVs can be recovered by adjusting the pH and ionic strength of the media, followed by an ultrafiltration step to remove ϵ-PL and achieve buffer exchange. The morphology, size, and protein composition of the ϵ-PL-precipitated BEVs are comparable to those purified by ultracentrifugation. Moreover, ϵ-PL-precipitated BEVs retained the biological activity as observed by confocal microscopy studies. And THP-1 cells stimulated with these BEVs undergo marked reprogramming of their transcriptome. KEGG analysis of the differentially expressed genes showed that the signal pathways of cellular inflammatory response were significantly activated. Taken together, we provide a new method to rapidly enrich BEVs with high purity and bioactivity, which has the potential to be applied to BEVs-related immune response studies.

Gallium is deemed to be a critical mineral given its irreplaceable use in consumer electronics and clean energy technologies. China has become a significant consumer of gallium while also playing a leading role in global gallium production, accounting for more than 90% of the world’s primary output in recent years. However, the quantification and evolution of China’s gallium cycle is limited until now. This study aims to uncover the dynamic flows and stocks of gallium in China during the period of 2005–2020. The results reveal that: 1) From 2005 to 2020, China’s gallium demand increased more than 20-fold, as a result of the booming semiconductor industry and the surging use of gallium in some low-carbon technologies; 2) despite the inefficient recovery issues existed in the production stage, the supply of gallium extracted as a byproduct grew in tandem with the capacity of alumina production, resulting in a significant supply surplus of 948 t by 2020; 3) China exported nearly half of its gallium mainly as raw materials and final products, but still experienced a high reliance on imported gallium-containing intermediate products from abroad, such as integrated circuits; 4) the generations of in-use stocks and end-of-life flows of gallium have accelerated since 2005 and reached about 278 t and 169 t in 2020, respectively. These indicate a large amount of available secondary gallium resource, with nonexistent recycling. The results provide a basis for identifying gallium extraction, use, loss and recycling within its anthropogenic cycle in China, as well as guidance for stakeholders to make future decisions concerning ways to improve resource efficiency and promote sustainable gallium practices from a dynamic material cycle perspective.

Fast and accurate diagnosis and the immediate isolation of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are regarded as the most effective measures to restrain the coronavirus disease 2019 (COVID-19) pandemic. Here, we present a high-throughput, multi-index nucleic acid isothermal amplification analyzer (RTisochip™-W) employing a centrifugal microfluidic chip to detect 19 common respiratory viruses, including SARS-CoV-2, from 16 samples in a single run within 90 min. The limits of detection of all the viruses analyzed by the RTisochip™-W system were equal to or less than 50 copies·μL−1, which is comparable to those of conventional reverse transcription polymerase chain reaction. We also demonstrate that the RTisochip™-W system possesses the advantages of good repeatability, strong robustness, and high specificity. Finally, we analyzed 201 cases of preclinical samples, 14 cases of COVID-19-positive samples, 25 cases of clinically diagnosed samples, and 614 cases of clinical samples from patients or suspected patients with respiratory tract infections using the RTisochip™-W system. The test results matched the referenced results well and reflected the epidemic characteristics of the respiratory infectious diseases. The coincidence rate of the RTisochip™-W with the referenced kits was 98.15% for the detection of SARS-CoV-2. Based on these extensive trials, we believe that the RTisochip™-W system provides a powerful platform for fighting the COVID-19 pandemic.

Background: Shotgun metagenomics has been used clinically for diagnosing infectious diseases. However, most technical assessments have been limited to individual sets of reference standards, experimental workflows, and laboratories. Methods: A reference panel and performance metrics were designed and used to examine the performance of shotgun metagenomics at 17 laboratories in a coordinated collaborative study. We comprehensively assessed the reliability, key performance determinants, reproducibility, and quantitative potential. Findings: Assay performance varied significantly across sites and microbial classes, with a read depth of 20 millions as a generally cost-efficient assay setting. Results of mapped reads by shotgun metagenomics could indicate relative and intra-site (but not absolute or inter-site) microbial abundance. Interpretation: Assay performance was significantly impacted by the microbial type, the host context, and read depth, which emphasizes the importance of these factors when designing reference reagents and benchmarking studies. Across sites, workflows and platforms, false positive reporting and considerable site/library effects were common challenges to the assay's accuracy and quantifiability. Our study also suggested that laboratory-developed shotgun metagenomics tests for pathogen detection should aim to detect microbes at 500 CFU/mL (or copies/mL) in a clinically relevant host context (10^5 human cells/mL) within a 24h turn-around time, and with an efficient read depth of 20M. Funding: This work was supported by National Science and Technology Major Project of China (2018ZX10102001).

Previous literature has associated math literacy with linguistic factors such as verbal ability and phonological skills. However, few studies have investigated "linguistic synchrony," shown in mathematical discussions. This study modelled math literacy and examined the relationship of math literacy with linguistic synchrony between students and facilitators. We retrieved data from 20,776 online mathematical discussion threads at a secondary school level. First, we assessed students' math literacy based on their discussions and classified them into high- and low-math literacy groups. Then, we conducted Cross-Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The result implies that students with high math literacy are more likely to share common words (eg, mathematical terms) with facilitators. At the same time, they would paraphrase the facilitators' words rather than blindly mimic them as the exact sentences or phrases. On the other hand, students with low math literacy tend to use overlapping words with facilitators less frequently and are more likely to repeat the exact same phrases from the facilitators. The findings provide an empirical data analysis and insights into mathematical discussions and linguistic synchrony. In addition, this paper implies the directions to improve online mathematical discussions and foster math literacy.

PM2.1 was collected at urban and suburban elementary schools in Shanghai during two sampling periods in cold and warm seasons in 2007. Nine polycyclic aromatic hydrocarbons (PAHs) and ten nitro-polycyclic aromatic hydrocarbons (NPAHs) in PM2.1 were determined. During both seasons, the concentrations of PAHs and NPAHs at urban and suburban schools were not significantly different (p>0.05) and were higher in the cold period than in the warm period. According to the diagnostic ratios, PAHs and NPAHs at both schools were subject to the mixed effects of vehicle emission and coal combustion during both periods. Moreover, the results of the backward trajectory showed that PAHs and NPAHs were more susceptible to external polluted air masses in the cold period. At both urban and suburban schools, the inhalation cancer risk of PAHs and NPAHs in PM2.1 for children during elementary period was dozens of times of the acceptable risk level regulated by the U.S.EPA, highlighting the adverse impact of exposure to PAHs and NPAHs on the healthy development of children.

Nickel is a strategic mineral resource, with 65% of nickel being used in stainless steel. The situation in Ukraine starting in February 2022 has led to significant fluctuations in nickel prices, with prices of nickel products along the same chain affecting and passing through each other. Using systematic risk entropy and granger causality networks, we measure the volatility risk of trade prices of nickel products using the nickel industry chain trade data from 2000–2019 and explore the transmission patterns of different volatility risk prices from the industry chain perspective. The findings show that: (1) Nickel ore has the highest risk of import trade price volatility and a strong influence, but low risk transmission. Stainless steel has the highest trade price impact but is also subject to the strongest passive influence. (2) The Americas have a higher risk of trade price volatility but a weaker influence. The influence and sensitivity of trade prices is stronger in Asia and Europe. (3) Indonesia’s stainless steel export prices have a high rate of transmission and strong influence. Germany’s ferronickel export prices are highly susceptible to external influences and can continue to spread loudly. Russian nickel ore export prices are able to quickly spread their impact to other regions.

The input–output (IO) network is the quantitative description of an IO-based economy in which nodes represent industries and edges connecting nodes represent the economic connection between industries. Robustness refers to the ability of tolerating perturbations that might affect the system’s functional body. There is both practical and theoretical significance to explore the robustness of the IO network for economic development. In this paper, we probe the robustness of the Chinese IO network based on the relative entropy of the probability distribution of network parameters (node degree, strongest path betweenness, downstream closeness and upstream closeness) under random node or edge failure and intentional node or edge attack. It is found that the Chinese IO network shows relatively weak robustness when it is under intentional attack, but relatively strong robustness when it is under random failure. Our experiment also verifies the applicability and effectiveness of the relative entropy model in measuring the robustness of the IO network.

In the context of the economic situation, international relations, and the consequences of COVID-19, the future competition pattern of crude oil trade is uncertain. In this paper, the crude oil international import competition and export competition networks are based on a complex network model. The link prediction method is used to construct a crude oil competition relationship prediction model. We summarize the evolving characteristics of the competitive landscape of the global crude oil trade from 2000 to 2019 and explore the reasons for the changes. Finally, we forecast the future potential crude oil import and export competition. The results indicate the following. (1) The crude oil import competition center is transferred from Europe and America to the Asia–Pacific region and it may continue to shift to developing regions. (2) At present, the competition among traditional crude oil exporters is the core of crude oil export competition, such as OPEC, Canada, and Russia. The United States has become the world’s largest crude oil exporter, which means that the core of crude oil export competition has begun to shift to emerging countries. The competition intensity of emerging crude oil exporters is gradually increasing. There is likely to be fierce export competition between traditional and emerging exporters. (3) In the future crude oil competition, we should pay attention to the trend of the United States, which may lead to the restructuring of the global oil trade pattern. Finally, this paper considers the exporters and importers and puts forward policy suggestions for policymakers to deal with the future global crude oil trade competition.

Abstract Although culture-based identification of bacteria is the gold-standard for the diagnosis of infectious diseases, it is time consuming. Recent advances in molecular diagnostics and microfluidic technologies have opened up new avenues for rapid detection of bacteria. Here, we describe a centrifugal-microfluidic chip for the detection of bacteria by integrating the cell lysis, clarification, and loop-mediated amplification (LAMP). The major advantages of this chip are as follows. Firstly, bacteria lysis was innovatively achieved by rotating a pair of magnets to generate bead-beating while the chip was kept stationary during lysis, which simplified the chip design because no additional valve was needed. Secondly, the on-chip assay time was short (within 70 min), which was competitive in emergency situations. Thirdly, results of the analysis can be interpreted by using a fluorescence detector or by the naked-eye, making it versatile in many areas, especially the resource-limited areas. The on-chip limits of detection of six types of bacteria were valued by gel electrophoresis, showing the similar results compared to the bench-top LAMP protocol. This chip can be used for rapid, sensitive, accurate and automated detection of bacteria, offering a promising alternative for simplifying the molecular diagnostics of infectious diseases.

PM2.5 (fine particles with diameters 2.5 micrometers and smaller) and PM>2.5 were separately collected in Kanazawa, Japan in every season, from the spring of 2017 to the winter of 2018, and nine polycyclic aromatic hydrocarbons (PAHs) and six nitropolycyclic aromatic hydrocarbons (NPAHs) were respectively determined using high-performance liquid chromatography (HPLC) with fluorescence and chemiluminescence detections. The atmospheric concentrations of both the PAHs and NPAHs showed seasonal changes (highest in the winter and lowest in the summer), which differed from the variations in the total suspended particulate matter (TSP) and PM2.5 amounts (which were highest in the spring). The contributions of major sources to the combustion-derived particulate (Pc) in the PM2.5 were calculated using the 1-nitropyrene-pyrene (NP) method, using pyrene and 1-nitropyrene as the representative markers of PAHs and NPAHs, respectively. The annual average concentration of Pc accounted for only 2.1% of PM2.5, but showed the same seasonal variation as PAHs. The sources of Pc were vehicles (31%) and coal heating facilities/industries (69%). A backward trajectory analysis showed that the vehicle-derived Pc was mainly from Kanazawa and its surroundings, and that coal heating facilities/industry-derived Pc was transported from city areas in central and northern China in the winter, and during the Asian dust event in the spring. These results show that large amounts of PAHs were transported over a long range from China during the winter. Even in the spring, after the coal heating season was over in China, PAHs were still transported to Japan after Asian dust storms passed through Chinese city areas. By contrast, the main contributors of NPAHs were vehicles in Kanazawa and its surroundings. The recent Pc concentrations were much lower than those in 1999. This decrease was mostly attributed to the decrease in the contribution of vehicle emissions. Thus, the changes in the atmospheric concentrations of Pc, PAHs and NPAHs in Kanazawa were strongly affected not only by the local emissions but also by long-range transport from China.

Social and emotional learning (SEL) has been increasingly emphasized in educational research and practice for promoting children's wellbeing. Perhaps the most well-known SEL framework is the model provided by the Collaborative for Academic, Social, and Emotional Learning (CASEL). This framework has become the dominant framework informing state SEL standards, which guide educators' SEL related practice. Yet, the content of these standards has not been formally examined for alignment with the CASEL framework. The purpose of this exploratory study was to evaluate the content of CASEL-based state standards to explore how the CASEL framework is translated into school-based practice and to understand what SEL skills are valued by states. Using text mining and network analysis, we identified SEL standard content that was consistent across nine states and examined relationships within and across CASEL domains. Implications of these findings for research and practice are discussed.

As instruction shifts away from traditional approaches, online learning has grown in popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics methods such as machine learning have been used by educational scholars to support online learners on a large scale. However, the fairness of AI prediction in educational contexts has received insufficient attention, which can increase educational inequality. This study aims to fill this gap by proposing a fair logistic regression (Fair-LR) algorithm. Specifically, we developed Fair-LR and compared it with fairness-unaware AI models (Logistic Regression, Support Vector Machine, and Random Forest). We evaluated fairness with equalized odds that caters to statistical type I and II errors in predictions across demographic subgroups. The results showed that the Fair-LR could generate desirable predictive accuracy while achieving better fairness. The findings implied that the educational community could adopt a methodological shift to achieve accurate and fair AI to support learning and reduce bias.

Social media has the unique capacity to expose many learners to media literacy instruction "via" targeted campaigns. Investigating learner engagement and reaction to these efforts may be a fruitful endeavor for researchers that can inform the design of future campaigns. However, the massive datasets associated with social media posts are difficult, and often impossible, to analyze with traditional qualitative methods. This study seeks to address this problem by leveraging machine learning techniques to collect and analyze Big Data from two different media literacy campaigns on the youth-oriented social media platform TikTok. Specifically, we explore the ways topic modeling, sentiment analysis, and network analysis can provide insight into learner engagement with these campaigns and discuss limitations and implications for stakeholders interested in utilizing these approaches.

After nationwide school closures due to COVID-19, virtual learning environments (VLE) have seen tremendous increase in usage. The current study identified teacher activities for orchestration using an Algebra VLE during school closures, and whether these activities were related to student achievement. In May 2020, we collected survey data on how 213 teachers were using a VLE for Algebra with 10,590 students, along with system logs and student achievement data. Results indicated that teachers made several changes to teacher strategies due to school closures, including allowing students more time to complete assignments. Multilevel modeling showed that teacher orchestration activities, particularly those related to regulation/management and awareness/assessment, were positively related to student achievement. We discussed the results and provided implications for practice (Q&A setting, assignment flexibility).

The unparalleled heterogeneity in genetic causes of hearing loss along with remarkable differences in prevalence of causative variants among ethnic groups makes single gene tests technically inefficient. Although hundreds of genes have been reported to be associated with nonsyndromic hearing loss (NSHL), GJB2, GJB6, SLC26A4, and mitochondrial (mt) MT-RNR1 and MTTS are the major contributors. In order to provide a faster, more comprehensive and cost effective assay, we constructed a DNA fluidic array, CapitalBioMiamiOtoArray, for the detection of sequence variants in five genes that are common in most populations of European descent. They consist of c.35delG, p.W44C, p.L90P, c.167delT (GJB2); 309kb deletion (GJB6); p.L236P, p.T416P (SLC26A4); and m.1555A>G, m.7444G>A (mtDNA). We have validated our hearing loss array by analyzing a total of 160 DNAs samples. Our results show 100% concordance between the fluidic array biochip-based approach and the established Sanger sequencing method, thus proving its robustness and reliability at a relatively low cost.

This paper uses improved technology for dynamic strain measurement to investigate the dynamic strain signals of blasting wave action tested in the range of 8–16 cm from the central blast. Based on the blasting mechanism and on the analysis of signal characteristics, blasting waves are recognized and divided into three zones, namely, shock wave zone, stress wave zone, and gas-expanding zone. This paper studies the relationships between stress, strain, and time of every zone. The tensile and compressive stresses of stress wave are considered. After the blasting test, four cracks from the borehole center toward the minimum burden appear at the model surface. The relationship between blasting wave and concrete damage is analyzed.

As Google Analytics becomes increasingly popular, more detailed records of users’ behaviors can be captured and analyzed to better understand the performance of websites. However, current Google Analytics related research usually draws conclusions from rough estimation based on the observation of the dashboard or other basic statistical processing of the data. This study aims to provide a more accurate and informative analysis from both temporal and geospatial perspectives via clustering and GIS application. The results obtained from a resource website case study demonstrate that the proposed method is able to help web managers better examine the temporal effect on users’ visiting patterns based on accurate mathematical computation as well as provides more geographical insight into website performance through the constructed density measure and 3D graphic presentation. By offering in-depth quantitative information relying on mining data from web logs, such a study can help web stakeholders make better decisions on how to maintain and improve the websites, especially adjusting resources by considering temporal fluctuations and inequity in geographical distribution.

The joint surface is widely distributed in the rock, thus leading to the nonlinear characteristics of rock mass strength and limiting the effectiveness of the linear model in reflecting characteristics. The JRC/JCS model is the nonlinear failure criterion and generally believed to describe the characteristics of joints better than other models. In order to develop the numerical program for JRC/JCS model, this paper established the relationship between the parameters of the JRC/JCS and Mohr-Coulomb models. Thereafter, the numerical implement method and implementation process of the JRC/JCS model were discussed and the reliability of the numerical method was verified by the shear tests of jointed rock mass. Finally, the effect of the JRC/JCS model parameters on the shear strength of the joint was analyzed.

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship.ClinicalTrials.gov NCT00567827.

Participating in online communities has significant benefits to students learning in terms of students' motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support.