Automatically Assessing Students Performance with Smartphone Data

• URL: http://arxiv.org/abs/2209.05596v1
• Date: Wed, 6 Jul 2022 10:05:23 GMT
• Title: Automatically Assessing Students Performance with Smartphone Data
• Authors: J. Fernandes, J. S\'a Silva, A. Rodrigues, S. Sinche, F. Boavida
• Abstract summary: We present a dataset collected using a smartphone application (ISABELA) We present several tests with different machine learning models, in order to classify students' performance. It is shown that the created models can predict student performance even with data collected from different contexts.
• Score: 0.7069200904392647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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