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This article deeply explores the behavior and effect of online learning in ideological and political education in colleges and universities, firstly, it clarifies the mechanism of the occurrence of online ideological and political learning behavior, and constructs the corresponding indicators of learning behavior. Using CART tree and XGBoost model, the article ranks the feature importance of learning behaviors. It combines with Bayesian network to construct a comprehensive analysis model to explore the causal relationship between learning behaviors and learning effects. By analyzing the data of M online learning platform in 2021, the study found that resource learning features have the most significant impact on learning performance, especially the indicators of video viewing time, number of homework submissions and number of online discussions. The study results show that when learning resources are rich and professional, learning performance is significantly improved, providing an effective way to optimize the teaching quality of online Civics education.

Student performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educational data mining research. Here, this paper proposes a machine learning method for the prediction of student performance based on online learning. The critical thought is that eleven learning behavioral indicators are constructed according to online learning process, following that, through analyzing the correlation between the eleven learning behavioral indicators and the scores obtained by students online learning, we filter out those learning behavioral indicators that are weakly correlated with student scores, meanwhile, retain these learning behavior indicators being strongly correlated with student scores, which are used as the eigenvalue indicators. Finally, using the eigenvalue indicators to train the proposed logistic regress model with Taylor expansion. Experimental results show that the proposed logistic regress model defeats against the comparative models in prediction ability. Results also indicate that there is a significant dependency between students’ initiative in learning and learning duration, nevertheless, learning duration has a significant effect on the prediction of student performance.

Background The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. Methods We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. Results Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. Conclusions Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Recent studies on global performance indicators (GPIs) reveal the distinct power that nonstate actors can accrue and exercise in world politics. How and when does this happen? Using a mixed-methods approach, we examine the impact of the Aid Transparency Index (ATI), an annual rating and rankings index produced by the small UK-based NGO Publish What You Fund. The ATI seeks to shape development aid donors' behavior with respect to their transparency—the quality and kind of information they publicly disclose. To investigate the ATI's effect, we construct an original panel data set of donor transparency performance before and after ATI inclusion (2006–2013) to test whether (and which) donors alter their behavior in response to inclusion in the ATI. To further probe the causal mechanisms that explain variations in donor behavior we use qualitative research, including over 150 key informant interviews conducted between 2010 and 2017. Our analysis uncovers the conditions under which the ATI influences powerful aid donors. Our mixed-methods evidence reveals how this happens. Consistent with Kelley and Simmons's central argument that GPIs exercise influence via social pressure, we find that the ATI shapes donor behavior primarily via direct effects on elites: the diffusion of professional norms, organizational learning, and peer pressure.

Background Iron deficiency (ID) limits the neurodevelopmental potential of more than 200 million children each year. Iron therapy started when IDA is first diagnosed—typically by screening for anemia or detection of clinical symptoms of IDA at 12 months of age—does not fully correct earlier ID-mediated brain dysfunction, underscoring the need for low-cost, easily implementable adjunct therapies to iron to treat or prevent this dysfunction in high-risk populations. Supplementation with the essential nutrient choline lessens damage done to the developing hippocampus when given with iron in pre-clinical rodent models, and choline supplementation improves hippocampus-mediated memory and learning in 2–3-year-old children with Fetal Alcohol Spectrum Disorders, a condition associated with hippocampal damage and one for which ID is a component of the neuropathology. Choline has not been tested in children with IDA. Our overall aim is to conduct a randomized, placebo-controlled clinical trial to test whether nine months of daily choline supplementation along with standard iron therapy improves hippocampus-dependent neurobehavioral outcomes in Ugandan infants with IDA. Methods Three hundred 6-month-old infants with IDA who present to immunization clinics at Mulago and Kawempe National Referral Hospitals in Kampala, Uganda, will be randomized to iron plus choline or iron plus placebo. Iron (oral ferrous sulfate 2 mg/kg/day) will be given for the first 3 months of follow-up, and a dispersible tablet of choline (200 mg as choline bitartrate) or identical placebo will be given daily for all 9 months of follow-up. We will conduct neurobehavioral tests assessing hippocampus-specific memory and attention and global cognition at enrollment (when each infant is 6 months of age) and after 9 months of follow-up (when each infant is 15 months of age). Discussion If we find a neurobehavioral benefit when choline is given along with iron, choline could be added immediately to standard of care treatment for IDA. This low-cost intervention could safely mitigate the brain dysfunction of early-life ID that is often not diagnosed until the hippocampal critical window is closing, providing life-long benefit for both the individual and the economic and social prosperity of entire regions. Trial registration Clinical trials.gov NCT06527391. Registered on 24 July 2024.

PurposeThe purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).Design/methodology/approachThrough literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.FindingsThe behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).Originality/valueThis study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

Neuromodulator release alters the function of target circuits in poorly known ways. An essential step to address this knowledge gap is to measure the dynamics of neuromodulatory signals while simultaneously manipulating the elements of the target circuit during behavior. Patriarchi et al. developed fluorescent protein–based dopamine indicators to visualize spatial and temporal release of dopamine directly with high fidelity and resolution. In the cortex, two-photon imaging with these indicators was used to map dopamine activity at cellular resolution. Science , this issue p. eaat4422 Genetically encoded indicators allow optical measurement of dopamine release in vivo at high spatiotemporal resolution. Neuromodulatory systems exert profound influences on brain function. Understanding how these systems modify the operating mode of target circuits requires spatiotemporally precise measurement of neuromodulator release. We developed dLight1, an intensity-based genetically encoded dopamine indicator, to enable optical recording of dopamine dynamics with high spatiotemporal resolution in behaving mice. We demonstrated the utility of dLight1 by imaging dopamine dynamics simultaneously with pharmacological manipulation, electrophysiological or optogenetic stimulation, and calcium imaging of local neuronal activity. dLight1 enabled chronic tracking of learning-induced changes in millisecond dopamine transients in mouse striatum. Further, we used dLight1 to image spatially distinct, functionally heterogeneous dopamine transients relevant to learning and motor control in mouse cortex. We also validated our sensor design platform for developing norepinephrine, serotonin, melatonin, and opioid neuropeptide indicators.

This study develops and tests a model of the relationship between a learning-oriented organizational climate, employee individual resilience and three broad categories of proactive behaviors, i.e. proactive work behavior, proactive strategic behavior and proactive person–environment fit behavior. The study tests a mediation model. Cross-sectional data was gathered from 108 employees in four Dutch organizations. Results demonstrate that employee resilience mediates the relationships between a learning-oriented organizational climate and proactive work behaviors. By investigating three proactive behaviors, this study answers to the call for studies that empirically investigate multiple related proactive behaviors within one study design. This design sheds light on whether a learning-oriented organizational climate promotes certain proactive behaviors more than others.

Video is one of the fastest growing online services offered to consumers. The rapid growth of online video consumption brings new opportunities for marketing executives and researchers to analyze consumer behavior. However, video also introduces new challenges. Specifically, analyzing unstructured video data presents formidable methodological challenges that limit the use of multimedia data to generate marketing insights. To address this challenge, the authors propose a novel video feature framework based on machine learning and computer vision techniques, which helps marketers predict and understand the consumption of online video from a content-based perspective. The authors apply this framework to two unique data sets: one provided by MasterClass, consisting of 771 online videos and more than 2.6 million viewing records from 225,580 consumers, and another from Crash Course, consisting of 1,127 videos focusing on more traditional education disciplines. The analyses show that the frame-work proposed in this article can be used to accurately predict both individual-level consumer behavior and aggregate video popularity in these two very different contexts. The authors discuss how their findings and methods can be used to advance management and marketing research with unstructured video data in other contexts such as video marketing and entertainment analytics.

PurposeAdvances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).Design/methodology/approachFirst, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.FindingsThe results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.Originality/valueThe evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.