Comparing Academic and Non-Academic Support Services: Mechanisms Impacting Academic Performance
Bibliographic record
Abstract
Learning support services, categorized into academic and non-academic support, have been demonstrated to positively influence student development. However, the distinct mechanisms by which academic and non-academic support services impact academic performance remain underexplored in China. This study seeks to provide comprehensive insights into these mechanisms. A survey was conducted with a sample of 1,234 students to gather data on learning support services, student engagement, and learning performance. Structural equation modeling (SEM) was employed to analyze the conceptual model derived from the survey data. Results revealed that student engagement played a partial mediating role between academic support services and learning performance, while it played a complete mediating role between non-academic support services and learning performance. Furthermore, the analysis of standardized coefficient values reveals that academic support services have a lesser impact on student engagement and learning performance compared to the influence of non-academic support services on these same outcomes. It is suggested that distance education institutions need to pay more attention to non-academic support services and optimize resource allocation to achieve more efficient and rational resource distribution. As numerous traditional face-to-face educational institutions in China expand into online education, they encounter challenges due to isolation between teachers and students. Consequently, the concept of learning support services in distance education has become a significant concern. The findings of this study could provide valuable insights for these institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".