Learning Behavior Research Articles

Dimension of Attitudes in Flipped Learning: Enjoyment, Engagement, Motivation, Behavioral Intention, and Perception

This research investigates the influence of video-based flipped learning on the attitudes of students in non-English language programs, focusing on enjoyment, engagement, motivation, behavioral intentions, and perception. The study employed a descriptive research design, collecting data through questionnaires administered to 30 students enrolled in non-English language courses. The findings reveal a significantly positive attitude towards flipped learning, with high scores in enjoyment and engagement indicating a preference for the interactive and dynamic nature of video-based learning environments. The study confirms that motivation and behavioral intentions are positively affected by the flipped model, enhancing students' proactive learning behaviors and overall engagement with the course material. Moreover, the positive perception of video-based learning among students suggests that this method supports a deeper understanding and retention of language skills. This research contributes to the educational discourse by demonstrating that flipped learning models can significantly improve learning experiences and outcomes in non-English language settings. Future studies are encouraged to explore the long-term impacts of flipped learning on language proficiency and retention, aiming to validate and expand upon the current findings.

Reflective Groupwork For Introductory Proof-Writing Courses

We discuss two proof evaluation activities meant to promote the acquisition of learning behaviors of professional mathematics within an introductory undergraduate proof-writing course. These learning behaviors include the ability to read and discuss mathematics critically, reach a consensus on correctness and clarity as a group, and verbalize what qualities “good” proofs possess. The first of these two activities involves peer review and the second focuses on evaluating the quality of internet resources. All of the activities involve groupwork and reflective discussion questions to develop students' experience with these practices of professional mathematics.

Push, Pull and Mooring Factors on Offline-Online Learning Switching Behavior

During the pandemic, many sectors, including education, were affected. The shift from traditional to online learning is an opening for students and educators to explore online learning. Students may connect from anywhere at any time through this learning mode. This sudden shift has impacted the learning behavior of students to a large extent. People can access information anytime and anywhere that is typically available only through a traditional classroom. This study adopts the Push-Pull-Mooring (PPM) theory as the theoretical framework to understand how push, pull, and mooring factors affect students' shift from offline to online learning. A survey was used as the main instrument in this study. A quantitative approach was utilized to achieve the stated research objectives. The questionnaires were distributed among undergraduate students in public and private schools in Malaysia through convenience sampling techniques. The minimal 77 sample size has been determined by utilizing G*Power software. 117 responses were collected from the questionnaires that met the minimum required sample size for this study. The findings emphasize that push and pull factors are essential to student learning. However, the mooring factor does not affect the student's switching behavior. The study sheds light on capturing more essential measures in the theoretical development of switching behavior.

Minimal motifs for habituating systems

Habituation-a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld-is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

EFL Teachers’ Emotions Toward Online-Merge-Offline Teaching Mode in University

The Online-Merge-Offline (OMO) teaching mode has emerged as a prominent new approach in language education reform with the expansion of online teaching in China. Due to the emotional complexity involved in educational reforms, this study explores the emotions expressed by Chinese EFL teachers in OMO classes, investigates the situational causes, and examines emotion regulation strategies in English Language Teaching. A questionnaire was distributed to 48 Chinese EFL teachers teaching General English (GE) using OMO mode across three normal universities, followed by semi-structured interviews for deeper insights. After collecting and analyzing data from questionnaires and interviews, the findings revealed a spectrum of emotions were experienced by EFL teachers, with positive emotions prevailing, particularly in GE classes. Love, surprise, and joy were prominent across different settings. Situational triggers encompassed classroom dynamics, institutional factors and social influences; reflecting teachers’ beliefs and identities, students’ learning engagement, behavior and academic achievements, colleagues and administrators’ work support and emotional support, policy makers’ reform mandate, and the Confucian heritage cultures. To regulate positive and negative emotions, Chinese EFL teachers primarily employed appraisal, followed by situation modification and selection. This study underscores the importance of emotional awareness for both students’ learning achievement and teachers’ professional development in ELT. It encourages Chinese EFL teachers to recognize the significance of their emotions in teaching effectiveness and offers insights for educators and administrators into supporting teachers’ emotional well-being and professional growth.

Haploinsufficiency of Syngap1 in striatal indirect pathway neurons alters motor and goal-directed behaviors in mice.

SYNGAP1 is a high-confidence autism spectrum disorder (ASD) risk gene and mutations in SYNGAP1 lead to a neurodevelopmental disorder (NDD) that presents with epilepsy, ASD, motor developmental delay, and intellectual disability. SYNGAP1 codes for Ras/Rap GTP-ase activating protein SynGAP (SynGAP). In mice, SynGAP is located in the postsynaptic density of glutamatergic synapses and regulates glutamate receptor trafficking in an activity-dependent manner. In addition to forebrain glutamatergic neurons, Syngap1 is highly expressed in the striatum, although the functions of SynGAP in the striatum have not been extensively studied. Here we show that Syngap1 is expressed in both direct and indirect pathway striatal projection neurons (dSPNs and iSPNs) in mice of both sexes. In a mouse model of Syngap1 haploinsufficiency, dendritic spine density, morphology, and intrinsic excitability are altered primarily in iSPNs, but not dSPNs. At the behavioral level, SynGAP reduction alters striatal-dependent motor learning and goal-directed behavior. Several behavioral phenotypes are reproduced by iSPN-specific Syngap1 reduction and, in turn, prevented by iSPN-specific Syngap1 rescue. These results establish the importance of SynGAP to striatal neuron function and pinpoint the indirect pathway as a key circuit in the neurobiology of SYNGAP1-related NDD.Significance statement SYNGAP1 mutations cause a neurodevelopmental disorder presenting with intellectual disability, motor problems, epilepsy, autism spectrum disorder, and a constellation of other behavioral and psychiatric conditions. SynGAP protein is highly expressed in the striatum but its functions in this brain region have not yet been explored. This study shows that loss of one copy of the Syngap1 gene from striatal indirect, but not direct, pathway neurons alters synaptic properties, cellular excitability, motor behaviors, and goal-directed responding in mice. This work provides a new perspective on the functions of SynGAP and suggests that altered activity in striatal circuits may be an important driver of the motor and learning alterations in people with SYNGAP1 disorder.

Educational Expectations and Academic Persistence among Rural Adolescents: The Protective Role of High Self-Esteem.

Rural adolescents are at higher risk of reduced academic persistence due to socioeconomic barriers. Educational expectations are theoretically viewed as important for adolescents' learning behaviors, and cross-sectional research has supported this assumption. However, few longitudinal studies have investigated the influence of educational expectations on adolescents' academic persistence. In addition, research has not clearly identified whether self-esteem moderates this link among adolescents who experience greater economic risk. Using data from two time points (i.e., six months apart), this study aims to provide a more complete understanding of whether, and under what conditions, rural adolescents' educational expectations influence academic persistence. The participants consist of 631 adolescents (Mage = 13.34 years at T1), and all the adolescents are from families with rural household registrations. The results show that the interaction term of educational expectations and self-esteem significantly predicts academic persistence. Specifically, after controlling for baseline academic persistence, educational expectations positively predict later academic persistence for rural adolescents with lower self-esteem, and educational expectations do not significantly predict later academic persistence for those with higher self-esteem. This study reveals the protective role of self-esteem in rural adolescents. High self-esteem benefits rural adolescents by protecting them from the effects of lower educational expectations on academic persistence. This finding also emphasizes the importance of developing self-esteem interventions for rural adolescents with low educational expectations to prevent them from experiencing weaker academic persistence.

Chaotic or crowded? The role of physical household environment in children's learning during the COVID-19 pandemic

Household chaos and crowding are known to significantly influence children's development; however, their relative contributions remain less clear. Furthermore, previous research has primarily focused on children's socioemotional and cognitive development, with limited attention to the learning process. This study explores the relation of household chaos and crowding with children's learning behaviors and emotions during the COVID-19 pandemic in Hong Kong. A sample of 262 parents and their primary school-aged children were recruited in Hong Kong. Both parents and children completed separate online questionnaires. Structural equation modeling revealed that while household chaos was correlated with crowding, it was specifically chaos, rather than crowding, that negatively related to children's learning behaviors. Importantly, the direct relationship between household chaos and children's learning behaviors remained significant even after accounting for family socioeconomic status (SES) and parents' involvement. This study enhances our understanding of the relation of physical environment with children's learning, emphasizing the distinct role of household chaos, separate from crowding and family SES.

A Student Education Data Mining Method based on Student Sequential Behaviors and Hybrid Recurrent Neural Network

This research aims to propose a student education data mining method based on a hybrid recurrent neural network and improved support vector machine-decision tree algorithm through in-depth analysis of student behavior sequences. The method combines the feature extraction capability of a hybrid recurrent neural network and the nonlinear mining efficiency of support vector machine-decision tree algorithm to achieve efficient prediction of students’ learning behavior and performance. Experimental results have shown that the designed method completed an APA of 91.3% and 89.2% for the HR-SDT model on the Student_1 and Student_2 datasets, respectively. The F1 score average values of the HR-SDT model reached 86.7% and 81.9%, respectively. The results indicate that the student behavior data mining method based on hybrid recurrent neural networks can accurately predict the learning behavior and performance of students, providing valuable insights and decision support for educators.

Optineurin regulates motor and learning behaviors by affecting dopaminergic neuron survival in mice

Optineurin (OPTN) is an autophagy receptor that participates in the degradation of damaged mitochondria, protein aggregates, and invading pathogens. OPTN is closely related to various types of neurodegenerative diseases. However, the role of OPTN in the central nervous system is unclear. Here, we found that OPTN dysregulation in the compact part of substantia nigra (SNc) led to motor and learning deficits in animal models. Knockdown of OPTN increased total and phosphorylated α-synuclein levels which induced microglial activation and dopaminergic neuronal loss in the SNc. Overexpression of OPTN can't reverse the motor and learning phenotypes. Mechanistic analysis revealed that upregulation of OPTN increased α-synuclein phosphorylation independent of its autophagy receptor activity, which further resulted in microglial activation and dopaminergic neuronal loss similar to OPTN downregulation. Our study uncovers the crucial role of OPTN in maintaining dopaminergic neuron survival and motor and learning functions which are disrupted in PD patients.

Data Mining Technology for Smart Campus in Behavior Association Analysis of College Students

The data in smart campuses is complex and massive, with insufficient utilization, and existing data processing methods have many limitations. Therefore, in order to improve the efficiency of data processing in universities and assist in student management, a data processing method integrating cluster analysis and association rule mining is proposed. The proposed method is divided into two parts. Firstly, an improved K-Means model based on information entropy and density optimization is constructed for clustering analysis of student consumption, learning, and other data; Secondly, use the improved Mapping Apriori to obtain the correlation between student grades, consumption records, and learning behavior. The clustering results on student consumption data show that the average accuracy of ED-K-Means clustering is 97.41%, which is 12.8%, 8.5% and 4.0% higher than the comparison algorithm. The result of the correlation between consumption level and achievement shows that when the amount of consumption is less than 1500 yuan, the student’s achievement is directly proportional to the amount of consumption. Therefore, the proposed method can effectively mine and analyze student behavior data, which has important practical significance for intelligent management in universities.

Behavioral Assays for Comprehensive Evaluation of Cognitive and Neuropsychiatric Comorbidities of Traumatic Brain Injury and Chronic Neurological Disorders.

Neurological deficits, psychiatric disorders, and cognitive impairments often accompany stroke, brain injury, epilepsy, and many neurological disorders, which present intricate comorbidities that challenge recognition and management. There are many tools and paradigms for evaluating learning, memory, anxiety, and depression-like behaviors in lab animal models of brain disorders. However, there is a significant gap between clinical observations and experimental models, which limit understanding of the complex interplay between chronic brain conditions and their impact on cognitive dysfunction and psychiatric impairments. This article describes an overview of experimental rationale, methods, protocols, and strategies for evaluating sensorimotor, affective and cognitive-associated comorbid behaviors in epilepsy, traumatic brain injury (TBI), stroke, spinal cord injury (SCI), and many other neurological disorders. First, we delve into clinical evidence elucidating the profound impact of comorbidities, e.g., psychiatric disorders and cognitive deficits, in individuals with epilepsy. Then, we discuss diverse approaches to assess these comorbidities in experimental models of brain diseases. Finally, we explore the methodologies for assessing motor function, sensorimotor, behavior, and psychiatric health. We cover strategies and protocols enabling these assays, including implementing behavioral paradigms to assess learning and memory, anxiety, and depression-like behaviors in rodents in health and disease conditions. It is essential to consider a comprehensive battery of tests to investigate various behavioral deficits, considering environment, age, and sex differences relevant to the disease, such as TBI, SCI, epilepsy, stroke, and other complex neurological conditions. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.

DNA methylation signatures of prenatal socioeconomic position associated with 36-month language outcomes

BackgroundSocioeconomic position (SEP), which reflects one’s position in society and access to resources, is strongly tied to neurodevelopment and is associated with epigenetic changes. AIM: This study examined whether DNA methylation signatures of prenatal SEP, measured in birth samples, are associated with child neurodevelopmental outcomes at 36 months of age. METHODS: Prenatal SEP DNA methylation scores were derived using 97 placenta and 127 cord blood biospecimens in the Early Autism Risk Longitudinal Investigation cohort. Participants completed the Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) at 36 months of age. Generalized regression analyses, adjusting for maternal age and race, were performed to test the association between SEP methylation score, for each birth biospecimen type, and MSEL and VABS scores. RESULTS: Significant associations were observed between placenta SEP methylation score and MSEL Expressive Language outcomes (beta = −2.7, p = 0.046, 95 % CI [- 5.43, −0.05]) and Receptive Language outcomes (beta = −2.5, p = 0.037, 95 % CI [-4.82, −0.16]). In cord blood, methylation-SEP scores were significantly associated with Receptive Language outcomes (beta = −2.0, p = 0.037, 95 % CI [-3.85, −0.12]). No significant associations were observed with VABS scores. CONCLUSION: Our results confirm associations between prenatal SEP and early childhood language development using a novel empiric DNA methylation measure of exposure

SOCIOLINGUISTIC PROFILE RESEARCH PAPER

This research paper explores the sociolinguistic profiles of learners in a sixth-grade classroom, focusing on the intersections between language, society, and individual characteristics. The study investigates the linguistic diversity within a group of Uzbek and Russian pupils learning English as a foreign language, highlighting the role of factors such as language background, socioeconomic status, and cultural identity in shaping their learning experiences. Two distinct learner subgroups are identified: one group of bilingual Russian-speaking students from urban, educated backgrounds, and another group of Uzbek-speaking students from monolingual, low socioeconomic backgrounds. Through analysis of these subgroups, the paper examines the influence of sociocultural dynamics on language learning behaviors and challenges. The study provides pedagogical and assessment implications, aiming to offer educators insights into adapting instructional techniques to accommodate the diverse needs and backgrounds of their students. By addressing these sociolinguistic dimensions, this paper contributes to understanding how language variety and social context affect language education and the notion of pedagogical implications and assessment implications are discussed.

The effect of different visual feedback interfaces of music training games on speech rehabilitation in hearing-impaired children: An fNIRS study

Singing plays a critical role in enhancing musicality, sound discrimination, and attention, and proves advantageous for speech rehabilitation in children with hearing impairments. Computer-based training games are well-suited to the learning behaviors of children, with substantial evidence suggesting that music training augments speech training capabilities in this demographic. Despite this, there is a lack of detailed exploration into the design of interactive online music training interfaces tailored for these needs. This study investigates brain activation changes using two visual feedback singing games, analyzed through functional near-infrared spectroscopy: a serious game (SG) and an entertainment game (EG) with visually enhanced feedback. It also assesses the efficacy of home-based music training software for speech rehabilitation.Methods involved recording oxygenated hemoglobin concentration (Delta [HbO]) signals from the prefrontal cortex, motor cortex, occipital lobe, and temporal lobe in 21 children (average age: 9.3 ± 1.9 years) during two singing interface experiments. Subjects also completed the Intrinsic Motivation Inventory (IMI) questionnaire post-experiment.Main results showed that brain regions, particularly the temporal lobe, exhibited stronger and more pronounced activation signals with the SG interface compared to the EG, suggesting that SG is more effective for speech system rehabilitation. The Intrinsic Motivation Scale results revealed higher acceptability for SG than for EG. This study provides insights into designing online speech rehabilitation products for children with hearing impairment, advocating for better interactive training methods from a neuroscience perspective.

A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm’s performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms.

Students' Behavior in the Learning Process Using Zoom Meeting Media: Problems and Solutions

Recent digital technologies have increased the flexibility of learning. Online learning is now seen as a good alternative, not a force anymore. Among all online learning media that can aid, Zoom is an interactive online learning media mainly used by lecturers to carry out synchronous learning processes when offline lectures cannot be carried out. Using Zoom, lecturers and students can interact synchronously, like face-to-face learning in class. Many studies have been conducted to see the impacts of online learning on students’ learning behaviors using various media, yet studies on how students behave while learning using Zoom have not yet been explored in more detail. This research aims to reveal how students behave in the learning process by using Zoom in English classes through a survey study. Data were collected through a questionnaire delivered online to some 142 English Department students of Universitas Negeri Padang who experienced online learning. They voluntarily took part in this survey. Carrying quantitative analysis, the research showed that most students did not follow the Zoom-mediated learning process as well as they did face-to-face learning, which was carried out offline, for various reasons. Several positive and negative behaviors were found when implementing the learning process using Zoom. Therefore, for the learning process to run well, it is necessary to agree on the ethics of the learning process by using Zoom. The findings of this research can provide a reference for making conventional ethics of online learning using Zoom or other media.

The Impact of Learning Behavior and Student Engagement on Learning Outcomes in Geography Subjects at Junior School Level

Geography is a science that deals with all activities and symptoms found on earth. At the junior high school level, geography is integrated into integrated social studies subjects. Geography is also an important social science to be implemented in lower secondary schools, but in its application there are also factors for its success, including learning behavior and student involvement. The objectives of this research are1) Analyzing the effect of learning behavior on student geography learning results, 2) Analyzing the effect of student engagement on geography learning results, and 3) Analyzing the effect of learning behavior and student engagement on geography learning outcomes. This research used quantitative method. The population in this study were students from grade 7 to grade 9 at SMP Negeri 2 Kemalang totaling 584 students. The sample in this study consisted of 237 students. SThe sampling was conducted using the stratified random sampling technique. Data were obtained using observation, documentation, and questiinnaires. The data analysis techniques used in this study include descriptive statistic, simple correlation tests, and multiple correlation analysis. The result shiwed that 1) students have a level of learning behavior in the high category, 2) student learning engagement in geography subjects categorized into high category, and 3) There is no significant influence of learning behavior and student engagement on geography learning success. It can be observed that the r value is smaller than the r tabke value 0.059 0.134) and the significance value is above 0,05 (0.416 0.05).

A 3D approach to understanding heterogeneity in early developing autisms.

Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Empowering Principals for Lifelong Learning: Self-directed Approaches in Digitalized Information Systems

The study delves into the dynamic interplay between digitalized information systems, competencies, self-directed learning, and lifelong learning in the context of the contemporary educational landscape. With the integration of Artificial Intelligence (AI) and evolving competencies becoming integral to education, understanding their combined impact on individuals' attitudes toward lifelong learning is paramount. Past research has explored these elements individually, but a comprehensive examination of their interconnected relationships remains scarce. The primary purpose is to investigate how AI integration, competencies, and self-directed learning collectively influence lifelong learning attitudes. The study aims to uncover the intricate dynamics by exploring the impact of digitalized information systems on competencies, the mediating role of self-directed learning, and the overall implications for lifelong learning behaviors. Utilizing a quantitative approach, the study focuses on teachers in China, distributing 500 questionnaires and receiving 340 responses. The research design incorporates a cross-sectional survey methodology, employing a structured questionnaire to gather data on AI integration, competencies, self-directed learning, and lifelong learning attitudes. Preliminary findings reveal significant correlations between AI integration, competencies, self-directed learning, and lifelong learning attitudes. The study observes the mediating role of self-directed learning, highlighting its importance in shaping the relationship between digitalized information systems, competencies, and the inclination toward lifelong learning. This research contributes to the theoretical understanding of the complex relationships in contemporary education. Its originality lies in integrating AI integration, competencies, and self-directed learning into a comprehensive framework.