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Previous research has proposed a conceptual model of the relationships between motivation, emotions, cognition, and metacognition related to students' learning performance and validated it in a blended learning context that combines traditional learning with the use of educational technology. This study examined these relationships to test the hypothesis that the levels of some factors in a face-to-face learning environment differ from those in a blended learning context, finding that the conceptual model is valid for both settings. The role of positive emotions was significant in blended learning but not in face-to-face learning. Furthermore, help-seeking and peer learning were relevant only in blended learning. A positive relationship between academic motivations and metacognitive strategies was found in the face-to-face context, whereas one from motivations to metacognitive and cognitive strategies was found in the blended context. Negative emotions were present in both learning settings. This shows that the role of emotions is relevant to students' perceived success.

Several studies have focused on identifying the significant behavioral predictors of learning performances in web-based courses by examining the log data variables of learning management systems, including time spent on lectures, the number of assignments submitted, and so forth. However, such studies fail to quantify the impact of emotional, motivational, behavioral, and cognitive-metacognitive factors simultaneously. This research was an attempt to understand the relations between students' motivation, cognitive-metacognitive strategies, behavior, and learning performance in the context of blended courses in higher education. Then, relevant predictors are used to obtain a model to classify the students' performance and to identify those who are at risk of failing the course. The authors conducted an empirical study in a higher educational course with 137 Mexican students. Nineteen variables related to emotions, motivation, cognitive-metacognitive strategies, and behavior. Only six were found to be significant. These variables explain approximately 67% of the variance between each student's overall grade. The model, based on those variables, correctly classifies 96% of the students.

Several investigations have focused on the relationship between personality and learning performance. However, the relationships among personality traits, motivation, emotional and behavioral engagement, and metacognitive-cognitive strategies remain unexplored. This research introduces a model based on a personality system set comprising metacognition, cognition, motivation, emotional and behavioral engagement. Five core education theories support this model. Three hundred seventy-four Mexican university students who were enrolled in a Biology and Industrial Management bachelor's degree program participated. Personality traits mainly affect academic engagement but not motivation. Additionally, academic engagement mediates the relationship between personality traits and metacognitive-cognitive strategies. The findings show that agreeableness and neuroticism traits influence emotional engagement and disaffection. Consciousness and extraversion affect behavioral disaffection and engagement, respectively. Meanwhile, motivation affects emotional engagement, disaffection and metacognitive-cognitive strategies. Finally, openness does not affect any of the constructs. Hence, personality traits directly influence academic engagement and disaffection but not motivation and cognitive strategies. Plain language summary: Effects of Personality and Motivation on Students Several investigations have focused on the relationship between personality and learning performance. However, the relationship between personality traits and motivation, emotions, behaviours, and metacognition-cognition remains unexplored. This research introduces a model based on the personality systems set. Five core education theories support this model. Three hundred seventy-four Mexican university students participated, who are enrolled in Biology and Industrial Management Bachelor's degree. Personality mainly affects emotions and behaviours but not motivation. Also, academic engagement mediates between personality traits and metacognition and cognition. The findings show that agreeableness and neuroticism influence emotions. Consciousness and extraversion affect behaviours. Meanwhile, motivation affects emotions and metacognition cognition. Finally, openness did not affect anyone of the constructs. [ABSTRACT FROM AUTHOR]

In Mexico, approximately 504,000 students pursue a bachelor's degree by means of distance or blended programmes. However, only 42% of these students conclude their degree on time. In the context of blended learning, the focus of this research is to present a causal model, based on a theoretical framework, which describes the relationships concerning motivations, emotions, cognitive strategies, metacognitive strategies, and learning strategies, and their impact on learning performance. The results suggest that negative emotions play a meaningful role between expectancy (a component of motivation) and learning strategies. Also, the expectancy component of motivation positively influences metacognitive strategies. Concerning the relationship between cognition and metacognition, metacognitive strategies take preference concerning the relationship between metacognitive and learning strategies, supporting the theoretical hypothesis that metacognitive processes are on a higher plane than cognition, and affect cognitive process directly. Moreover, the learning outcomes are directly influenced by cognitive and learning strategies, but not by metacognitive ones. Similarly, motivation has direct effects on metacognitive and learning strategies but not on cognitive ones.

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students' learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.

Motivated by previous authors' work, where Shannon entropy, box covering and information dimension were applied to quantify pulmonary lesions, this paper extends such a contribution in two fashions: (i) Following the approach to quantify pulmonary lesions with Deng entropy and Deng information dimension obtained through box covering method; (ii) exploiting the Shannon and Deng lesion quantification for pulmonary illnesses classification with a bidirectional Long Short Term Memory (bLSTM). The referred pulmonary illnesses are Common Pneumonia (CP) and COVID-19. Shannon entropy and information dimension are performed here and called the Shannon sequence. Then, Deng entropy and Deng information dimension are computed for chest Computed Tomography (CT) images to obtain and combine two data sequences to quantify the pulmonary lesions. The data sequence resulting from the data combination is called the Deng sequence. Both Shannon and Deng sequences are independently used as input for the bLSTM. CT lung scans of 531 healthy subjects, 497 confirmed COVID-19 diagnoses and 516 with CP were analyzed to obtain the Shannon and Deng sequences. The results demonstrate that Deng entropy and Deng information dimension of CT images can differentiate similar lung lesions between COVID-19 and CP. Besides, a statistical analysis shows that: (a) Classification by the bLSTM is better when using the Deng sequence than the Shannon sequence; (b) Deng sequences plus bLSTM significantly outperform DenseNet-201, GoogLeNet and MobileNet-v2 in classifying COVID-19, CP and Normal CT (healthy subjects) in time and accuracy. Hence, the Deng sequence and bLSTM are fast and accurate tools for helping in diagnosing CP and COVID-19. [ABSTRACT FROM AUTHOR]

Simple Summary: The fractional ( q , q ′ )-information dimension for complex networks is introduce, and a dual version of the ( q , q ′ )-entropy, called ( q , q ′ )-extropy, is proposed. Experiments reveal that the fractional ( q , q ′ )-information dimension is less than the classical one (based on Shannon entropy) for both real-world and synthetic networks. This article introduces a new fractional approach to the concept of information dimensions in complex networks based on the ( q , q ′ )-entropy proposed in the literature. The q parameter measures how far the number of sub-systems (for a given size ε) is from the mean number of overall sizes, whereas q ′ (the interaction index) measures when the interactions between sub-systems are greater ( q ′ > 1 ), lesser ( q ′ < 1 ), or equal to the interactions into these sub-systems. Computation of the proposed information dimension is carried out on several real-world and synthetic complex networks. The results for the proposed information dimension are compared with those from the classic information dimension based on Shannon entropy. The obtained results support the conjecture that the fractional ( q , q ′ )-information dimension captures the complexity of the topology of the network better than the information dimension. [ABSTRACT FROM AUTHOR]

The quantification of learning acquisition in a blended and online course is still slightly explored from the complex systems lens. The fractional online learning rate (fOLR) using fractional integrals is introduced. The notion of fOLR is based on the nonlinearity of the individual students learning pathway network, built from Learning Management System log files. Several learning pathway networks from students that pass or fail the course were constructed. The Akaike information criterion shows that the minimum number of boxes to cover these networks follow a power-law model. Further analysis shows that the fOLR model and its parameters were significantly compared with the online learning rate model. Thus, the fOLR was computing power and delayed power models, inspired by the "law of practice." The results show that the fractional definition is a better model and has a nonlinear relationship with the overall grade. Also, engagement and disengagement mould the fOLR curve. It means that the student's performance is affected by the engagement, and it is necessary that they are encouraged to pay more effort and attention to the learning activities, and those activities need to be designed to be fun and pleasant to improve the learning achievements. [ABSTRACT FROM AUTHOR]