Sibling research has often emphasized resource dilution, treating siblings as competitors for parental resources. Using sibling-linked data from five waves of the China Family Panel Studies, this study examines the association between older siblings’ college attendance and younger siblings’ entry into higher education, its socioeconomic heterogeneity, and potential pre-entry pathways. Older siblings’ college attendance is positively associated with younger siblings’ entry into higher education across alternative specifications, with larger estimates among younger siblings with rural hukou and in families in which neither parent completed senior secondary education. In a complementary school-age sample, older siblings’ college attendance is associated with longer weekend study time, younger siblings’ higher education expectation, parents’ expectation for them, and greater educational investment. These findings qualify resource-dilution accounts by suggesting that older siblings’ educational experiences may generate sibling-mediated intragenerational family resource transferring, particularly in disadvantaged families, with implications for targeted college-going support.
In response to such problems as the disconnection between labor education and professional education in emerging engineering disciplines of higher vocational colleges, the confinement of digital-intelligent applications to the tool level, and the single-dimension curriculum evaluation, this study, taking digital-intelligent empowerment as the starting point, constructs a labor education curriculum system featuring “virtual-real integration and hierarchical progression” together with a whole-process evaluation mechanism characterized by “multi-stakeholder collaboration and digital-intelligent feedback”, and carries out practical exploration in the integrated circuit technology specialty cluster of Suzhou Vocational Institute of Industrial Technology. Practice shows that students’ digital-intelligent labor literacy and job adaptability have been improved, which can provide a reference for labor education reform in similar institutions.
Generative artificial intelligence (GenAI) complicates the assessment of learning in undergraduate academic writing courses: a polished paper does not necessarily reveal what students understand or can do independently. Prior work has examined writing portfolios, student–AI interactions, and broader assessment approaches, but instructors still need a practical way to connect evidence from writing activities to specific learning outcomes and judge what that evidence supports. This conceptual paper proposes an assessment-evidence-to-learning- outcome mapping framework for undergraduate academic writing instruction. Drawing on constructive alignment, assessment validity, portfolio scholarship, DRIVE, and the 4Ps model, it defines four adaptable course-level outcomes: problem formulation, argumentative reasoning, critical verification, and ethical responsibility. It maps product, process, performance, and practice evidence to these outcomes and illustrates how complementary sources can inform bounded assessment judgments through a constructed case and an illustrative rubric. The framework is a context-specific design proposal, not a validated instrument; its value and feasibility require empirical testing.
The latent nature of red cultural identity makes conventional evaluation approaches, which mainly rely on final examinations, questionnaires and teachers’ subjective impressions, inadequate for identifying the level at which students’ identity formation has stalled. Drawing on digital-intelligent teaching practice themed on Shanghai’s red publishing culture, this research operationalises red cultural identity into four dimensions: cognition, affect, values and behaviour. Process learning data are collected through knowledge-graph inquiry, virtual simulation, AI-assisted learning dialogue and project creation, forming a closed-loop framework of “data sensing — indicator calculation — intelligent diagnosis — feedback intervention”. This framework was tested via a six-week digital research-and-learning programme on red publishing during the founding period of the Communist Party of China. Practice shows that improved evaluation validity does not hinge on algorithmic complexity but on two revisions: eliminating easily accessible but weakly explanatory indicators such as click counts and online duration, and acknowledging the limited validity of automated recognition for the affective and value dimensions while reserving interpretive authority for teachers. The evaluation of red cultural identity can be datafied, yet the right of interpretation should not be ceded to technical systems.
Blended learning in vocational colleges requires coordination between institutional decisions and daily teaching practices. This study examines institutional management from the perspectives of administrators, faculty members, and students, and explores the relationship between management practices and perceived student learning outcomes. Survey data were obtained from 862 respondents across three public vocational colleges in Nanjing, including 152 administrators, 330 faculty members, and 380 students. All three groups assessed six dimensions of institutional management, while faculty members and students (n = 710) also evaluated five dimensions of perceived learning outcomes. Descriptive statistics, group comparisons, and Pearson correlation analyses were employed. The findings revealed generally favorable assessments of institutional management, with no statistically significant differences among the three respondent groups at the dimension level. Faculty and students also reported relatively positive perceptions of learning outcomes. The use of evaluation findings to inform instructional improvement received comparatively lower ratings from administrators (2.55), faculty (2.47), and students (2.49), indicating an area for further institutional attention. All 30 correlations between management practices and perceived learning outcomes were very weak and statistically nonsignificant (r = −0.064 to 0.062). These findings highlight the importance of translating institutional monitoring and evaluation into practical instructional improvements, while recognizing the limitations of perception-based measures in establishing relationships with student learning outcomes.
