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A novel student dataset for ML based effective career growth recommendation
Savitha Acharya1,2,3, Surendra Shetty4, Niranjan N Prabhu5
1Department of Electronics and Communication Engineering, Nitte (Deemed to be University), NMAM Institute of Technology (NMAMIT), Nitte, Karkala, Karnataka, India.
Scientific Reports
|June 24, 2026
Summary
This study introduces a new benchmark dataset for Educational Data Mining (EDM) to support personalized career recommendations for engineering students, addressing data challenges for better career growth insights.
Area of Science:
- Computer Science
- Education
- Data Science
Background:
- Educational Data Mining (EDM) techniques analyze student data for career path prediction.
- A significant challenge is the absence of a benchmark dataset for this purpose.
- Identifying key student attributes for career growth decision support is difficult.
Purpose of the Study:
- To address the need for a comprehensive dataset for personalized career growth recommendations.
- To facilitate research in applying EDM to engineering student career development.
- To overcome limitations in existing data for career path prediction.
Main Methods:
- Curated and preprocessed a novel benchmark dataset.
- Included student demographics, academic background, skills (technical and soft), and stress factors.
- Managed data heterogeneity, sparsity, and noise using cleaning, feature engineering, and dimensionality reduction.
Main Results:
- Developed a well-structured, novel benchmark dataset.
- Successfully managed common data challenges like heterogeneity and sparsity.
- The dataset is ready for use in EDM research for career recommendations.
Conclusions:
- The new benchmark dataset will advance research in personalized career growth recommendations.
- Effective data preprocessing is crucial for utilizing student data in EDM.
- This dataset supports the development of data-driven career guidance for engineering students.
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