Negative Academic Emotion and Psychological Well-being in Chinese Rural-to-Urban Migrant Adolescents: Examining the Moderating Role of Cognitive Reappraisal

Whether and How Family Functioning Relates to the Development of Self-Compassion and Emotion Regulation in Chinese Migrant Children? A Random Intercept Cross-Lagged Panel Analysis

Given the heightened difficulties in social adjustment and the potential diminishment of social networks encountered by migrant children, family functioning may play a crucial role in their development. Existing research has highlighted the significance of family environment in shaping adolescent self-compassion and emotion regulation, which can serve as protective factors against adverse emotional outcomes. However, there remains a lack of comparative studies to examine the specific effects of family functioning on fostering self-compassion and emotion regulation in both migrant and their non-migrant counterparts. The present study utilized a three-wave longitudinal design with 12-month intervals to examine the longitudinal effects of family functioning on self-compassion and emotion regulation, while also examining potential variations in these associations between migrant and non-migrant children. A total of 244 migrant children and 491 non-migrant children from a high school in Guangdong Province (357 females; Mage = 15.3 at Time 1, SDage = 0.53) participated in this study. Random-intercept cross-lagged panel models (RI-CLPMs) were utilized to examine the longitudinal associations among family functioning, self-compassion, and emotion regulation in both groups. The results showed that, at the within-person level, family functioning reciprocally predicted self-compassion over time among migrant children, and it also exerted an indirect effect on emotion regulation, mediated by self-compassion. Among non-migrant children, emotion regulation positively predicted self-compassion over time, with no other observed cross-lagged effects.

Detection and analysis of graduate students' academic emotions in the online academic forum based on text mining with a deep learning approach

Purpose

The possibility of mental illness caused by the academic emotions and academic pressure of graduate students has received widespread attention. Discovering hidden academic emotions by mining graduate students' speeches in social networks has strong practical significance for the mental state discovery of graduate students.

Design/methodology/approach

Through data collected from online academic forum, a text based BiGRU-Attention model was conducted to achieve academic emotion recognition and classification, and a keyword statistics and topic analysis was performed for topic discussion among graduate posts.

Findings

Female graduate students post more than male students, and graduates majoring in chemistry post the most. Using the BiGRU-Attention model to identify and classify academic emotions has a performance with precision, recall and F1 score of more than 95%, the category of PA (Positive Activating) has the best classification performance. Through the analysis of post topics and keywords, the academic emotions of graduates mainly come from academic pressure, interpersonal relationships and career related.

Originality

A BiGRU-Attention model based on deep learning method is proposed to combine classical academic emotion classification and categories to achieve a text academic emotion recognition method based on user generated content.

The mediating role of general academic emotions in burnout and procrastination among Chinese medical undergraduates during the COVID-19 pandemic: A cross-sectional study

Background

Academic procrastination has become more prevalent during the COVID-19 pandemic due to teaching/learning changes. This phenomenon induces academic burnout, which is already serious among medical students. However, the academic emotion, which is the factor most vulnerable to changes in the academic environment, is still unknown. Therefore, the current study aimed to investigate the mediating role of general academic emotions in procrastination and burnout among Chinese medical students during the COVID-19 pandemic.

Methods

This cross-sectional study enrolled 995 medical students from China Medical University. We applied the Chinese version of the Maslach Burnout Inventory Student Survey (MBI-SS), the Aitken Procrastination Inventory (API) and the General Academic Emotion Questionnaire for College Students (GAEQ) to evaluate the variables of interest. We examined the mediation effects of GAEs by hierarchical linear regression analysis.

Results

Correlation analyses showed a significant positive correlation between procrastination and burnout. Procrastination and burnout positively and negatively correlated with negative academic emotions, respectively. Hierarchical linear regression analyses showed that procrastination had positive associations with negative academic emotions, while it had negative associations with positive academic emotions. The contributions (as mediators) of GAEs to burnout and procrastination were 21.16% (NAEs), 29.75% (PAEs), 54.25% (NDEs) and 23.69% (PDEs).

Conclusions

The results indicate that academic emotions had mediating effects on procrastination and burnout. Medical students' worries about the uncertainty of the learning environment may have exacerbated academic burnout. Targeted improvements in the teaching environment to communicate encouragement and reduce anxiety and helplessness among medical undergraduates for implementing medical education while preventing and controlling the infection.

Parental Migration and Psychological Well-Being of Children in Rural China

This paper empirically analyzes the impact of parental migration on the psychological well-being of children using ordered probit models based on a survey conducted among 1680 primary school students and their parents in Majiang County, Guizhou Province, China in 2020. The findings are as follows. First, compared with having no migrant parents, having two migrant parents significantly reduces the psychological well-being of children and having one migrant parent has no significant effect. Second, mediation analysis shows that parental migration reduces child depression by increasing household absolute and relative incomes. It also increases depression and reduces the subjective happiness of children by reducing parental discipline. However, it has no significant impact on parent–child interactions. Third, by dividing the sample by absolute and relative poverty, we find that the effect of parental migration on the psychological well-being of children varies with household economic conditions. Comparatively speaking, children from poor households are more affected by parental migration in terms of depression, whereas children from non-poor households are more affected by parental migration in terms of subjective happiness. This paper examines the transmission mechanism between parental migration and the psychological well-being of children, provides a perspective of household economic conditions for child psychology and offers useful insights for family education and government policymaking in this area.

Current status on the ability of the elderly in rural China: implications for future nursing and policy

Background

Aging is a global problem, and the ability assessment of the elderly plays an important role in the formulation of pension policies. It's necessary to evaluate the ability of the elderly in rural China to provide insights into future nursing care and policy making.

Methods

The elderly in 20 rural villages were selected by convenience sampling. We used "Elderly Ability Evaluation Form" issued by the Ministry of Civil Affairs of China as survey tool. The characteristics and score differences of the elderly of different ability level were compared and analyzed. And logistic regression analyses were conducted to identify the potential risk factors for disability in the elderly.

Results

A total of 2,878 elders were included, of which there were 1,916 elders with intact ability, 866 elders with mild disability, 42 elders with moderate disability, 54 elders with severe disability. The incidence of disability among respondents was 33.43%. There were significantly statistical differences in the dimensions of activities of daily living, mental state, perception and communication, and social participation among elders with intact ability, mild, moderate and severe disability (all P<0.05). The age, education level, marital status and living situations were all corrected to the scores on the activities of daily living, mental state, perception and communication, and social participation among elders (all P<0.05), and the elderly with age ≥75 years, illiteracy, unmarried and live alone had higher risk for disability (all P<0.05).

Conclusions

The current situation of the ability level of the elderly in rural China seems to be worrying, and it's necessary to establish a long-term nursing care system and aging policy to meet the needs of the elderly with regards to those potential influencing factors.

Academic Emotion Classification and Recognition Method for Large-scale Online Learning Environment-Based on A-CNN and LSTM-ATT Deep Learning Pipeline Method

Subjective well-being is a comprehensive psychological indicator for measuring quality of life. Studies have found that emotional measurement methods and measurement accuracy are important for well-being-related research. Academic emotion is an emotion description in the field of education. The subjective well-being of learners in an online learning environment can be studied by analyzing academic emotions. However, in a large-scale online learning environment, it is extremely challenging to classify learners’ academic emotions quickly and accurately for specific comment aspects. This study used literature analysis and data pre-analysis to build a dimensional classification system of academic emotion aspects for students’ comments in an online learning environment, as well as to develop an aspect-oriented academic emotion automatic recognition method, including an aspect-oriented convolutional neural network (A-CNN) and an academic emotion classification algorithm based on the long short-term memory with attention mechanism (LSTM-ATT) and the attention mechanism. The experiments showed that this model can provide quick and effective identification. The A-CNN model accuracy on the test set was 89%, and the LSTM-ATT model accuracy on the test set was 71%. This research provides a new method for the measurement of large-scale online academic emotions, as well as support for research related to students’ well-being in online learning environments.

The Mediating Role of Conceptions of Learning in the Relationship Between Metacognitive Skills/Strategies and Academic Outcomes Among Middle-School Students

This study investigated the mediating role of conceptions of learning in the relationship between metacognitive skills/strategies and academic outcomes among middle-school students. The self-report “Learning Conceptions Questionnaire” (LCQ) and “Metacognitive questionnaire on the method of study” (QMS—in Italian) were administered to 136 middle-school students and their academic outcomes were collected. Correlation analyses revealed that within metacognitive skills/strategies only self-assessment was positively correlated with academic outcomes. Mediation analysis indicated that a conception of learning as internal attribution of success and failure was significantly involved as mediator in the relationship between metacognitive skills/strategies and academic outcomes. This study permitted to advance our knowledge about the relationship between metacognitive skills/strategies and academic outcomes and it has opened the way to practical implications.