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Online learning environments have become increasingly popular due to their flexibility and convenience, but they also present new challenges, such as maintaining student motivation and engagement. To address these challenges, it is crucial to understand and predict students’ learning behaviors. This study explores the recognition and management of students’ learning behaviors through cognitive status analysis. By conducting a thorough analysis of students’ cognitive status and applying advanced deep learning models and algorithms, this study demonstrates the effectiveness of recognizing and managing students’ learning behaviors. The proposed model combines convolutional neural networks and long short-term memory networks with attention mechanisms, which incorporate cognitive status evaluation features and use them as filters for text information. The model’s focus on text sentences with distinctive features in cognitive status evaluation leads to more effective recognition and management of students’ learning behaviors. Additionally, by integrating Most Informative Propositions and Semantic Propositional Value into the deep learning model, this study achieved excellent results in cognitive status evaluation recognition tasks. Further experiments show that by mixing different features and using advanced algorithms, the final model achieves high classification accuracy and F1 scores on multiple types of learning behaviors. Continuous assessment of students’ cognitive status and learning behaviors can lead to the development of effective learning strategies and intervention measures, which can enhance students’ mastery of knowledge and overall performance.

It is widely assumed that data-driven models achieve good results only with sufficiently large training data, whereas process-based models are usually expected to be superior in data-poor situations. To investigate this, we calibrated several process-based and data-driven hydrological models using training datasets of observed discharge that differed in terms of both the number of data points and the type of data selection, allowing us to make a systematic comparison of the learning behaviour of the different model types. Four data-driven models (conditional probability distributions, regression trees, artificial neural networks, and long short-term memory networks) and three process-based models (GR4J, HBV, and SWAT+) were included in the testing, applied in three meso-scale catchments representing different landscapes in Germany: the Iller in the Alpine region, the Saale in the low mountain ranges, and the Selke in the transition between the Harz and central German lowlands. We used information measures (joint entropy and conditional entropy) for system analysis and model performance evaluation because they offer several desirable properties: they extend seamlessly from uni- to multivariate data, they allow direct comparison of predictive uncertainty with and without model simulations, and their boundedness helps to put results into perspective. In addition to the main question of this study – to what extent does the performance of different models depend on the training dataset? – we investigated whether the selection of training data (random, according to information content, contiguous time periods, or independent time points) plays a role. We also examined whether the shape of the learning curve for different models can be used to predict the achievable model performance based on the information contained in the data and whether using more spatially distributed model inputs improves model performance compared to using spatially lumped inputs. Process-based models outperformed data-driven ones for small amounts of training data due to their predefined structure. However, as the amount of training data increases, the learning curve of process-based models quickly saturates, and data-driven models become more effective. In particular, the long short-term memory network outperforms all process-based models when trained with more than 2–5 years of data and continues to learn from additional training data without approaching saturation. Surprisingly, fully random sampling of training data points for the HBV model led to better learning results than consecutive random sampling or optimal sampling in terms of information content. Analysing multivariate catchment data allows predictions about how these data can be used to predict discharge. When no memory was considered, the conditional entropy was high. However, as soon as memory was introduced in the form of the previous day or week, the conditional entropy decreased, suggesting that memory is an important component of the data and that capturing it improves model performance. This was particularly evident in the catchments in the low mountain ranges and the Alpine region.

Purpose The purpose of this study is to explore the role of employees’ underlying implicit person theories in the relationship with innovative work climate and proactive behaviour at work. First, the authors study how an employee’s implicit person theory (IPT), or the domain-general implicit belief about the development potential of people’s attributes, relates to learning goal orientation and proactive learning and entrepreneurial behaviour at work. Second, the authors investigate how employees’ perception of their work climate is associated with this IPT. Design/methodology/approach The authors set up an exploratory study relying on survey data from a sample of 498 professionally active Flemish adults and analysed a correlational path through SEM. Findings The authors found that holding an incremental IPT (i.e. believing in the development potential of people’s attributes) positively relates to proactive learning and entrepreneurial behaviour. Moreover, the authors found that employees working in an innovative work climate are more likely to hold an incremental IPT. Originality/value This study offers indications that IPT is a relevant explanatory variable in the relationship between innovative work climate on the one hand and learning goal orientation, learning work behaviour and entrepreneurial work behaviour on the other hand. As such, this study suggests that IPT is a promising concept that can be actively endorsed as a relevant underlying psychological process variable for fostering learning and entrepreneurial behaviour in organizations.

Purpose The purpose of this paper is to establish whether all the dimensions of individual learning behavior matter for self-employment practice among youths, using evidence from Uganda. Design/methodology/approach This study is a correlational and cross-sectional type. A questionnaire survey of 393 youths was used. The data collected were analyzed through SPSS. Findings The results indicate that meaning-oriented learning behavior, planned learning behavior and emergent learning behavior do matter for self-employment practice among youths in Uganda unlike instruction-oriented learning behavior. Research limitations/implications This study focused on self-employed youths who have gone through tertiary education in Uganda. Therefore, it is likely that the results may not be generalized to other settings. The results show that to promote self-employment practice among youths, the focus should be put mainly on meaning-oriented learning behavior, planned learning behavior and emergent learning behavior. Originality/value This study provides initial evidence on whether all the dimensions of individual learning behavior do matter for self-employment practice among youths using evidence from an African developing country – Uganda.

With the popularity of online education, multiple technology-based educational tools are gradually being introduced into online learning. The role of gamification in online education has been of interest to researchers. Based on learners’ visual, auditory, and kinesthetic (VAK) learning styles, this study uses an empirical research method to investigate the behavioral intention of students to participate in online gamified classrooms in selected universities located in Guangdong province and Macao. The main contributions of this study are to focus on the impact that differences in learning styles may have on the behavioral intentions of learners and to include the “perceived learning task” as an external variable in the theoretical framework. The main research findings are: perceived usefulness and enjoyment are partially mediated between VAK learning styles and the intention to participate in online gamified classrooms; and perceived learning tasks are partially mediated between perceived usefulness and the intention to participate in online gamified classrooms. According to the findings and the Technology Acceptance Model (TAM), this study constructs the Technology-Learning Behavior Acceptance Model (T-LBAM) to explore the intrinsic influencing factors of students’ intention to participate in gamified online classes and makes suggestions for future online gamification teaching.

Background Neurological complications may occur in patients with acute or chronic renal failure; however, in cases of acute renal failure, the signs and symptoms are usually more pronounced, and progressed rapidly. Oxidative stress and nitric oxide in the hippocampus, following kidney injury may be involved in cognitive impairment in patients with uremia. Although many women continue taking hormone therapy for menopausal symptom relief, but there are also some controversies about the efficacy of exogenous sex hormones, especially estrogen therapy alone, in postmenopausal women with kidney injury. Herein, to the best of our knowledge for the first time, spatial memory and synaptic plasticity at the CA1 synapse of a uremic ovariectomized rat model of menopause was characterized by estradiol replacement alone. Results While estradiol replacement in ovariectomized rats without uremia, promotes synaptic plasticity, it has an impairing effect on spatial memory through hippocampal oxidative stress under uremic conditions, with no change on synaptic plasticity. It seems that exogenous estradiol potentiated the deleterious effect of acute kidney injury (AKI) with increasing hippocampal oxidative stress. Conclusions Although, estrogen may have some positive effects on cognitive function in healthy subjects, but its efficacy in menopause subjects under uremic states such as renal transplantation, needs to be further investigated in terms of dosage and duration.

Background Understanding the relationship between a stimulus and how it is perceived reveals fundamental principles about the mechanisms of sensory perception. While this stimulus-percept problem is mostly understood for color vision and tone perception, it is not currently possible to predict how a given molecule smells. While there has been some progress in predicting the pleasantness and intensity of an odorant, perceptual data for a larger number of diverse molecules are needed to improve current predictions. Towards this goal, we tested the olfactory perception of 480 structurally and perceptually diverse molecules at two concentrations using a panel of 55 healthy human subjects. Results For each stimulus, we collected data on perceived intensity, pleasantness, and familiarity. In addition, subjects were asked to apply 20 semantic odor quality descriptors to these stimuli, and were offered the option to describe the smell in their own words. Using this dataset, we replicated several previous correlations between molecular features of the stimulus and olfactory perception. The number of sulfur atoms in a molecule was correlated with the odor quality descriptors “garlic,” “fish,” and “decayed,” and large and structurally complex molecules were perceived to be more pleasant. We discovered a number of correlations in intensity perception between molecules. We show that familiarity had a strong effect on the ability of subjects to describe a smell. Many subjects used commercial products to describe familiar odorants, highlighting the role of prior experience in verbal reports of olfactory perception. Nonspecific descriptors like “chemical” were applied frequently to unfamiliar odorants, and unfamiliar odorants were generally rated as neither pleasant nor unpleasant. Conclusions We present a very large psychophysical dataset and use this to correlate molecular features of a stimulus to olfactory percept. Our work reveals robust correlations between molecular features and perceptual qualities, and highlights the dominant role of familiarity and experience in assigning verbal descriptors to odorants.

Given the crucial importance of engagement in learning English as a Foreign Language (EFL) and the increasing interest in its psychological dimensions, this study was an attempt to examine the effect of teacher support on engagement by considering the serial mediating roles of learning experience and motivated learning behaviour. Participants were 384 EFL learners chosen through multi-stage cluster sampling. The SEM results demonstrated that teacher support directly and positively predicted engagement. Additionally, teacher support affected engagement through the serial mediating roles of learning experience and motivated learning behaviour. Teachers can provide learners with substantial support and encouragement to enhance their learning experience, which could in turn considerably affect their motivated learning behaviour. Consequently, EFL learners who are motivated and willing to exert effort in learning and classroom activities would be more engaged in their learning process. Finally, important implications and suggestions for future research are presented.

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a superparameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s −1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

Background The restorative effect of sleep on waking brain activity remains poorly understood. Previous studies have compared overall neural network characteristics after normal sleep and sleep deprivation. To study whether sleep and sleep deprivation might differentially affect subsequent connectivity characteristics in different brain regions, we performed a within-subject study of resting state brain activity using the graph theory framework adapted for the individual electrode level. In balanced order, we obtained high-density resting state electroencephalography (EEG) in 8 healthy participants, during a day following normal sleep and during a day following total sleep deprivation. We computed topographical maps of graph theoretical parameters describing local clustering and path length characteristics from functional connectivity matrices, based on synchronization likelihood, in five different frequency bands. A non-parametric permutation analysis with cluster correction for multiple comparisons was applied to assess significance of topographical changes in clustering coefficient and path length. Results Significant changes in graph theoretical parameters were only found on the scalp overlying the prefrontal cortex, where the clustering coefficient (local integration) decreased in the alpha frequency band and the path length (global integration) increased in the theta frequency band. These changes occurred regardless, and independent of, changes in power due to the sleep deprivation procedure. Conclusions The findings indicate that sleep deprivation most strongly affects the functional connectivity of prefrontal cortical areas. The findings extend those of previous studies, which showed sleep deprivation to predominantly affect functions mediated by the prefrontal cortex, such as working memory. Together, these findings suggest that the restorative effect of sleep is especially relevant for the maintenance of functional connectivity of prefrontal brain regions.