|
|
|
|
|
Gomathy Ramaswami, Teo Susnjak and Anuradha Mathrani
Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students? performance, educators can detect at-risk stud...
ver más
|
|
|
|
|
|
|
|
|
|
|
Okan Bulut, Damien C. Cormier and Seyma Nur Yildirim-Erbasli
Traditional screening approaches identify students who might be at risk for academic problems based on how they perform on a single screening measure. However, using multiple screening measures may improve accuracy when identifying at-risk students. The ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Gomathy Ramaswami, Teo Susnjak and Anuradha Mathrani
Learning Analytics (LA) refers to the use of students? interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, presc...
ver más
|
|
|
|
|
|
|
|
|
|
|
Emanuel Marques Queiroga, Matheus Francisco Batista Machado, Virgínia Rodés Paragarino, Tiago Thompsen Primo and Cristian Cechinel
This paper describes a nationwide learning analytics initiative in Uruguay focused on the future implementation of governmental policies to mitigate student retention and dropouts in secondary education. For this, data from a total of 258,440 students we...
ver más
|
|
|
|
|
|
|
|
|
|
|
Warawut Narkbunnum and Kittipol Wisaeng
Depression is becoming one of the most prevalent mental disorders. This study looked at five different classification techniques to predict the risk of students? depression based on their socio-demographics, internet addiction, alcohol use disorder, and ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Balqis Albreiki, Tetiana Habuza, Zaid Shuqfa, Mohamed Adel Serhani, Nazar Zaki and Saad Harous
Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of ed...
ver más
|
|
|
|
|
|
|
|
|
|
|
Emanuel Marques Queiroga, João Ladislau Lopes, Kristofer Kappel, Marilton Aguiar, Ricardo Matsumura Araújo, Roberto Munoz, Rodolfo Villarroel and Cristian Cechinel
Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics researc...
ver más
|
|
|
|
|
|
|
|
|
|
|
Dave DeSimone, Donya Sharafoddinzadeh and Maryam Salehi
Lead (Pb) exposure can delay children?s mental development and cause behavioral disorders and IQ deficits. With children spending a significant portion of their time at schools, it is critical to investigate the lead concentration in schools? drinking wa...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ann Nosseir,Yahia Fathy
Pág. pp. 4 - 18
Identifying students at risk or potentials excellent students is increasingly important for higher education institutions to meet the needs of the students and develop efficient learning strategy. Early stage prediction can give an indication of the stud...
ver más
|
|
|
|
|
|
|
|
|
|
|
Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis and Omiros Ragos
Transferring knowledge from one domain to another has gained a lot of attention among scientists in recent years. Transfer learning is a machine learning approach aiming to exploit the knowledge retrieved from one problem for improving the predictive per...
ver más
|
|
|
|
|
|