Towards Development of Data Architecture for Learning Analytics Projects Using Data Engineering Approach

Abstract

Educational data mining and learning analytics are contemporary research disciplines that can provide interesting and hidden insights into the effectiveness of different learning styles, course complexity, learning content difficulties, and learning design issues. However, these two emerging research disciplines do not deal with the initial phases of data ingestion, preparation, and transformation, because the researchers often expect data to be available, grouped, and cleaned. Therefore, we aim to explore the possibilities of big data processing in education from the data engineering point of view. Further, we analyse a referenced data infrastructure model and discuss its appropriateness for developing an ML platform for learning analytics and educational data mining research at the university. As a result, we propose the ML platform for learning analytics research and emphasise the importance of suitable data infrastructure selection, as well as the impact of the individual steps of the data engineering life cycle, on the quality of the learning analytics model.