Abstract
Some research works point out that university students are increasingly presenting depression, anxiety, and even suicidal scenarios during their studies. Numbers indicate that these issues affect both undergraduate and graduate students, dropping their academic performance steadily. However, these disorders can be captured in advance through, for example, heart bit rates and changes in blood pressure. This paper presents the Academic Support Aid Proposal (ASAP), characterized by a model capable of getting data, such as location and body signals, from students and afterward provides some recommendations and advice to them during their period inside the university. The body signals give an emotional context from a specific student and his/her location. These elements help the ASAP in terms of accuracy to the recommendation approach, which is based upon IoT (smart bands) and a machine learning paradigm. The differentiated aspect of the present contribution is based on the use of ubiquitous computing and the proposed architecture.
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Di iorio Silva, G., Sergio, W.L., Ströele, V., Dantas, M.A.R. (2021). ASAP - Academic Support Aid Proposal for Student Recommendations. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_4
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