CBL - Campus del Baix Llobregat

Projecte llegit

Títol: Design and development of a students' performance predicting LMS utilizing Machine Learning based on mental stress level measured through a Bluetooth enabled smart watch

Estudiants que han llegit aquest projecte:

Director/a: BARRADO MUXÍ, CRISTINA

Departament: DAC

Títol: Design and development of a students' performance predicting LMS utilizing Machine Learning based on mental stress level measured through a Bluetooth enabled smart watch

Data inici oferta: 21-11-2022     Data finalització oferta: 21-06-2023


Estudis d'assignació del projecte:
    MU MASTEAM 2015
Tipus: Individual
 
Lloc de realització: EETAC
 
Segon director/a (UPC): REYES MUÑOZ, M. ANGÉLICA
Altres: Faiyaz Doctor (University of Essex, UK)
Departament 2n director/a:
 
Paraules clau:
machine learning, heart rate variability, smart watch, student performance
 
Descripció del contingut i pla d'activitats:
Stress and mental health problems can negatively impact numerous aspects of students' lives, resulting in degrading their academic achievement, quality of life, and social behavior. Numerous research suggests that depression is associated with lower academic performance of students. The aim of this research is twofold. Firstly, in order to establish a correlation between students' mental stress level and their academic performance, a dataset would be compiled through gathering the data by conducting a survey in a university located in Punjab, Pakistan. The questionnaires would be based on measuring the stress level of students using Perceived Stress Scale in addition to some other demographic questions. Afterwards, this dataset would be analyzed utilizing various Machine Learning algorithms. The second objective is to develop an innovative, affordable & smart performance predicting Learning Management System which would take into account student's stress & mental health while predicting the students' performance using Machine Learning. The stress level of the individuals would be gathered in real time through Bluetooth enabled smart watches. The smart watch would measure the stress level based on the Heart Rate Variability (HRV) of the student. The smart LMS would function in such a way that if the student would be under stress constantly for 7 days then his/her parents would receive an alert notifications. Smart recommendations would be given to the teachers/parents on ways to improve the performance of the students and to cope up with the mental stress suggestions like breathing exercises etc. Data mining could be further applied while considering solutions for students with different performance/stress levels.
 
Overview (resum en anglès):
Stress and academic anxiety problems can negatively impact numerous aspects of students¿ lives, resulting in degrading their academic achievement, quality of life, and social behaviour. Various research suggests that depression is associated with lower academic performance of students. The aim of this research is twofold. Firstly, in order to establish a correlation between students¿ mental stress level and their academic performance, a dataset has been compiled through gathering the data by conducting a survey in a university located in Punjab, Pakistan. The questionnaires were based on measuring the stress level of students using Perceived Stress Scale (PSS) , Cognitive performance assessment scale, in addition to some other demographic questions. Afterwards, this dataset has been analysed utilizing various machine learning algorithms. The second objective was to develop an innovative, affordable and smart performance predicting Learning Management System that takes into account students¿ mental stress while predicting the students¿ performance using machine learning models. The technique that was used for the mental stress measurements of the students was based on a phenomenon known as the Heart Rate Variability (HRV). A smart watch was utilized to measure the Heart Rate Variability of the students that was used to assess the stress level of students in academics. A Machine Learning (ML) model was trained using various parameters that were derived from the Heart Rate Variability. The original dataset that was used to train the model is known as Swell dataset. The SWELL dataset consists of HRV indices computed from the multimodal SWELL knowledge work dataset for research on stress and user modelling. The ML model effectively made prediction about the stress levels of the students with an accuracy of 98.1%.

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