ABSTRACT Although health-related behaviors such as sleep, physical activity, and mindful relaxation relate to self-concept, the links between these behaviors and self-concept clarity (SCC) have not been explored. Here, we use experience sampling with an undergraduate sample (N = 155) to investigate how these behaviors predict SCC within persons and between persons and whether their daily links with SCC are moderated by general habits and personality traits. Sleep duration predicted next-day SCC levels, and subjective sleepiness/alertness was linked with SCC at momentary, daily, and between-person levels. Physical activity and mindful relaxation behaviors did not relate to SCC on average, although trait conscientiousness moderated these relationships. No relationships were moderated by self-reported habits. Results suggest that sleep may be especially important for ongoing subjective self-evaluation.

The relations between health behaviors and self-concept clarity: An experience-sampling study

This study aims to explore the educational management practices associated with innovative teaching, focusing on their impact on the happiness of engineering college students. Through a comprehensive literature review and empirical research, the study analyzes the characteristics of innovative teaching, the learning tendencies of engineering students, the definition and measurement of student happiness, as well as the relationship between innovative teaching and students’ happiness. To collect data, a combination of quantitative questionnaire surveys and qualitative semi-structured interviews was employed. Data were gathered from 300 engineering college students through questionnaires and from 10 representative students through interviews. Through both statistical analysis and content analysis, this study unveils the implementation and effectiveness of innovative teaching among engineering students, as well as the happiness levels of these students, and the connection between innovative teaching and happiness. The research findings demonstrate that innovative teaching is widely adopted among engineering students and significantly enhances their academic performance and learning motivation. Engineering students generally exhibit a high degree of learning autonomy and creativity, actively engaging in practical projects. Furthermore, the happiness of engineering students is primarily reflected in dimensions such as satisfaction with academic achievements, motivation for learning, and support from their social networks. Further analysis reveals a positive correlation between innovative teaching and the happiness of engineering college students. Quantitative analysis establishes a significant positive correlation between innovative teaching and happiness, highlighting the positive impact of innovative teaching on the well-being of engineering students. Qualitative interview data delves into students’ experiences and cognitive changes brought about by innovative teaching, further affirming the beneficial effects of innovative teaching on their happiness.

pdf/innovative-teaching-and-the-education-management-practice-of-2lsuab5d37.pdf

Innovative teaching and the education management practice of improving the happiness of engineering college students

Many empirical networks originate from correlational data, arising in domains as diverse as psychology, neuroscience, genomics, microbiology, finance, and climate science. Specialized algorithms and theory have been developed in different application domains for working with such networks, as well as in statistics, network science, and computer science, often with limited communication between practitioners in different fields. This leaves significant room for cross-pollination across disciplines. A central challenge is that it is not always clear how to best transform correlation matrix data into networks for the application at hand, and probably the most widespread method, i.e., thresholding on the correlation value to create either unweighted or weighted networks, suffers from multiple problems. In this article, we review various methods of constructing and analyzing correlation networks, ranging from thresholding and its improvements to weighted networks, regularization, dynamic correlation networks, threshold-free approaches, and more. Finally, we propose and discuss a variety of key open questions currently confronting this field.

Correlation networks: Interdisciplinary approaches beyond thresholding

ABSTRACT Objective A holistic understanding of the naturalistic dynamics among physical activity, sleep, emotions, and purpose in life as part of a system reflecting wellness is key to promoting well-being. The main aim of this study was to examine the day-to-day dynamics within this wellness system. Methods Using self-reported emotions (happiness, sadness, anger, anxiousness) and physical activity periods collected twice per day, and daily reports of sleep and purpose in life via smartphone experience sampling, more than 28 days as college students (n = 226 young adults; mean [standard deviation] = 20.2 [1.7] years) went about their daily lives, we examined day-to-day temporal and contemporaneous dynamics using multilevel vector autoregressive models that consider the network of wellness together. Results Network analyses revealed that higher physical activity on a given day predicted an increase of happiness the next day. Higher sleep quality on a given night predicted a decrease in negative emotions the next day, and higher purpose in life predicted decreased negative emotions up to 2 days later. Nodes with the highest centrality were sadness, anxiety, and happiness in the temporal network and purpose in life, anxiety, and anger in the contemporaneous network. Conclusions Although the effects of sleep and physical activity on emotions and purpose in life may be shorter term, a sense of purpose in life is a critical component of wellness that can have slightly longer effects, bleeding into the next few days. High-arousal emotions and purpose in life are central to motivating people into action, which can lead to behavior change.

Within-Person Temporal Associations Among Self-Reported Physical Activity, Sleep, and Well-Being in College Students

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Within-Person Temporal Associations Among Self-Reported Physical Activity, Sleep, and Well-Being in College Students