The goal of Journal of DataLink (JDL) is to advance the field of data science education through a platform that provides high-quality research, innovative methodologies, and practical insights. Our aim is to bridge the gap between theoretical research and practical application, focusing on the educational value of data in real-world scenarios.
We welcome submissions on, but not limited to, the following topics:
- Real Data Analysis:
- Research utilizing real data from various fields such as healthcare, finance, social sciences, engineering, environmental sciences, etc.
- Case studies applying data science techniques to solve practical problems.
- Synthetic Data Generation:
- Studies on the creation and validation of synthetic datasets that accurately reflect the characteristics of real data.
- Comparative analysis of synthetic and real data in educational settings.
- Data Processing and Curation for Educational Purposes:
- Techniques for preprocessing and curating data for educational purposes.
- Datasets specifically designed for data science education.
- Innovative Teaching Methodologies:
- Development and assessment of teaching methodologies in data science education.
- Design and implementation of data science curricula across various educational levels.
- Modeling and Analytical Techniques:
- New modeling techniques and their applications in an educational context.
- Practical examples of data analysis using the latest tools and technologies.
- Educational Tools and Resources:
- Development of software, tools, and resources that enhance data science education.
- Reviews of existing educational tools and their impact on learning outcomes.
- Multimedia and Interactive Learning:
- Integration of video, interactive tutorials, and practical exercises in data science education.
- Studies on the effect of multimedia resources on learning engagement and performance.
