To design a data engineering pipeline that incorporates artificial intelligence techniques for processing and analyzing educational data.
To develop a predictive analytics model that can be used to predict student performance and outcomes in educational settings.
To implement the data engineering pipeline and predictive analytics model using appropriate tools and technologies.
Conduct a literature review on the current trends and techniques in AI-enabled data engineering for predictive analytics in education.
Design a data engineering pipeline that can collect, clean, and preprocess educational data for analysis.
Develop a predictive analytics model that can provide insights into student performance and outcomes.
Implement the data engineering pipeline and predictive analytics model using tools such as Python, TensorFlow, and Apache Spark.
Evaluate the performance of the data engineering pipeline and predictive analytics model using real-world educational data sets.
