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Drop-out Prediction.pdf

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Preview of Drop-out Prediction
🔗 Source: educationaldatamining.org
📊 Size: 515 KB
📄 Pages: 2 pages
⬇️ Downloads: 106

Summary

Students' drop-out rates are high, with over 40% not completing studies within 6 years, especially in technical courses. The study progress data can be transformed into a multivariate time series, and the dynamic time warping (DTW) distance can be used with the k-nn classifier or as an SVM kernel for drop-out prediction. The approach recognizes about 67% of drop-outs after the first semester and 60% after the second semester, with the k-nn parameter k = 11 being well-suited for evaluation.

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Description

Students' drop-out rates are high, with over 40% not completing studies within 6 years, especially in technical courses.

Technical Information

  • File Format: PDF
  • File Size: 515 KB
  • Pages: 2
  • Language: EN
  • Total Downloads: 106
  • Last Updated: 3 weeks ago

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This PDF document about Drop-out Prediction provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Drop-out Prediction.

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