  As the number of smart devices that surround us increases, so do the
opportunities to create smart socially-aware systems. In this context, mobile
devices can be used to collect data about students and to better understand how
their day-to-day routines can influence their academic performance. Moreover,
the Covid-19 pandemic led to new challenges and difficulties, also for
students, with considerable impact on their lifestyle. In this paper we present
a dataset collected using a smartphone application (ISABELA), which include
passive data (e.g., activity and location) as well as self-reported data from
questionnaires. We present several tests with different machine learning
models, in order to classify students' performance. These tests were carried
out using different time windows, showing that weekly time windows lead to
better prediction and classification results than monthly time windows.
Furthermore, it is shown that the created models can predict student
performance even with data collected from different contexts, namely before and
during the Covid-19 pandemic. SVMs, XGBoost and AdaBoost-SAMME with Random
Forest were found to be the best algorithms, showing an accuracy greater than
78%. Additionally, we propose a pipeline that uses a decision level median
voting algorithm to further improve the models' performance, by using historic
data from the students to further improve the prediction. Using this pipeline,
it is possible to further increase the performance of the models, with some of
them obtaining an accuracy greater than 90%.
