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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.

\"Could the terrorist attacks on the Twin Towers have been avoided? What about the recent global financial crisis? Behind these apparently very different events it is possible to identify a common element of organizational myopia - a syndrome that severely limits the capacity of organizations to foresee the effects of their own decisions and to recognize signs of danger or opportunity. Based on several case studies, Organizational Myopia explores the barriers that impede organizations from identifying an effective response to the problems which they have to confront. Using real-world cases, the author investigates the mechanisms that generate myopia in organizations at the individual, organizational and interorganizational level in contexts that are complex, uncertain, ambiguous and changeable. This book will help readers understand how to limit the origins of myopia and therefore increase the capacity of organizations to anticipate and contain unexpected events\"-- Provided by publisher.

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.

\"Everyone working in and with organizations will, from time to time, experience frustrations and problems when trying to accomplish tasks that are a required part of their role. This is an unusual routine - a recurrent interaction pattern in which someone encounters a problem when trying to accomplish normal activities by following standard organizational procedures and then becomes enmeshed in wasteful and even harmful subroutines while trying to resolve the initial problem. They are unusual because they are not intended or beneficial, and because they are generally pervasive but individually infrequent. They are routines because they become systematic as well as embedded in ordinary functions. Using a wide range of case studies and interdisciplinary research, this book provides researchers and practitioners with a new vocabulary for identifying, understanding, and dealing with this pervasive organizational phenomenon, in order to improve worker and customer satisfaction as well as organizational performance\"-- Provided by publisher.

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.

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.

\"Neuroscientists are discovering the Chemistry of Culture by revealing the neurological links between our brain and our relationships. This book brings that brain research out of the lab and into schools by connecting it to highly effective culture-building strategies\" -- Provided by publisher.

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.