Abstract:
More and more, the needs of academic data
analysis are requiring in educational settings for the
purpose of improving student learning and
institutional effectiveness. In the education world,
testing and data are driving decisions for what
knowledge and skills students should be learning and
how students’ learning relates to their learning
outcomes. On that occasion, a better option for
building a machine learning model is to get effective
data preprocessing concepts. For these reasons, this
paper describes the very first step of the main research
work which considers the correlations between
students’ academic performance, behavior and
personality traits to reveal the presence of an
intriguing way. Intuitively, this paper proposes the
uses of Extraction-Transformation-Loading (ETL) in
the preprocessing stage to collect and analyze of
students’ data from multiple data sources. In this
system, data is collected from multiple data sources
based on the structures which are used as a testbed.
Students’ demographic data and assessment results
from Student Information System (SIS), logs of their
interaction with Moodle are used for data collection.
Then aggregating with Web logs also captures student
behavior that is represented by daily summaries of
student clicks based on courses and by their actions.
