Machine learning analysis of factors affecting college students’ academic performance
2024

Factors Affecting College Students' Academic Performance

Sample size: 1101 publication 10 minutes Evidence: moderate

Author Information

Author(s): Lu Jingzhao, Liu Yaju, Liu Shuo, Yan Zhuo, Zhao Xiaoyu, Zhang Yi, Yang Chongran, Zhang Haoxin, Su Wei, Zhao Peihong

Primary Institution: Hebei Agricultural University

Hypothesis

This study aims to explore various key factors influencing the academic performance of college students.

Conclusion

The study found that metacognitive awareness, learning motivation, and participation in learning are crucial for academic success.

Supporting Evidence

  • Metacognitive awareness significantly influences academic performance.
  • Learning motivation is a key factor in students' academic success.
  • Participation in learning activities correlates with better academic outcomes.
  • Time management and mental health also impact students' academic achievements.
  • Professional training enhances students' overall competencies.

Takeaway

This study shows that how well college students do in school depends on their awareness of their own learning, how motivated they are, and how much they participate in their studies.

Methodology

The study used chi-square tests and machine learning models (LOG, SVC, RFC, XGBoost) to analyze factors affecting academic performance.

Potential Biases

The study may not generalize well due to the specific context of Chinese universities.

Limitations

The study's small sample size may not accurately reflect academic performance across various fields, and it did not consider general education courses.

Participant Demographics

Participants included first to fourth-year students from various science and engineering majors at Hebei Agricultural University.

Statistical Information

P-Value

p<0.01

Statistical Significance

p<0.01

Digital Object Identifier (DOI)

10.3389/fpsyg.2024.1447825

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