Enhancing Students’ Self-Directed Learning Ability in Political Courses through Small Private Online Course: A Case Study of Fuzhou Software Vocational and Technical College
Bibliographic record
Abstract
This study explores the effectiveness of the Small Private Online Course (SPOC) model in enhancing self-directed learning (SDL) in political courses at Fuzhou Software Vocational and Technical College. The research objectives were 1) to investigate the effect of the SPOC teaching model in enhancing students’ self-directed learning abilities in political courses at Fuzhou Software Vocational and Technical College, 2) to investigate the SPOC teaching model implementation in political courses and analyze its impact on students’ self-directed learning abilities, and 3) to explore students’ perspectives towards the SPOC teaching model in promoting their self-directed learning abilities in political courses. A quantitative research design assessed changes in students’ SDL skills and academic performance before and after SPOC implementation. Findings reveal that SPOC significantly improves SDL skills, enabling students to manage their learning better, develop critical thinking, and achieve higher academic outcomes. This research contributes to understanding online and self-directed learning by introducing a novel application of SPOC in vocational education. It also highlights innovative teaching methods and assessment strategies that enhance the effectiveness of SDL in political courses. The study offers practical implications for educators and policymakers, emphasizing SPOC’s potential as a transformative tool in digital-era education. Further exploration of SPOC’s application across different educational systems could offer valuable insights into the impact of cultural, infrastructural, and pedagogical factors, guiding the refinement of implementation strategies and promoting best practices globally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".