Zobrazeno 1 - 10
of 78
pro vyhledávání: '"student at risk"'
Autor:
Elvira N. Gilemkhanova
Publikováno v:
Теоретическая и экспериментальная психология, Vol 17, Iss 3, Pp 160-184 (2024)
Background. Presented study shows the potential of using nonlinear analysis algorithms as an opportunity for a comprehensive study of the socio-psychological characteristics of students who are and are not assigned to the risk groups for drug addicti
Externí odkaz:
https://doaj.org/article/dd4382065215451b97b2a25ad8ab0b25
Autor:
Lay Guat Chan, Qian Yun Ng
Publikováno v:
STEM Education, Vol 4, Iss 2, Pp 151-164 (2024)
It is becoming increasingly evident that educators need to prioritize the welfare of their students, particularly those who are underperforming academically, also known as "students at risk". By analyzing learning behaviors, including attendance reco
Externí odkaz:
https://doaj.org/article/d70c44f12c0b45be9fb36d5f6f4f0c72
Publikováno v:
Ho Chi Minh City Open University Journal of Science - Social Sciences, Vol 14, Iss 2, Pp 17-32 (2024)
A qualitative study on understanding student dropout risk was developed due to the increasing number of students leaving school at Dalaguete National High School, Dalaguete, Cebu, Philippines, a problem that the school faces and probably the rest of
Externí odkaz:
https://doaj.org/article/483fef9431274a0f8595018dd90f1f32
Autor:
Meliza Putri, Wahyu Hidayat
Publikováno v:
Edification Journal, Vol 6, Iss 1, Pp 75-88 (2023)
Adolescence is a time when humans search for their own identity. Many of them cannot find their identity as a result of some of their influences. Until now, various forms of juvenile delinquency are often found. However, it is difficult to eliminate
Externí odkaz:
https://doaj.org/article/e40688afc1ff4c989419790596891915
Autor:
Sojourner, Aaron1
Publikováno v:
Economic Journal. Jun2013, Vol. 123 Issue 569, p574-605. 32p. 7 Charts, 2 Graphs.
Publikováno v:
Career Development Quarterly. Jun2007, Vol. 55 Issue 4, p313-327. 15p.
Autor:
Naveed Anwer Butt, Zafar Mahmood, Khawar Shakeel, Sultan Alfarhood, Mejdl Safran, Imran Ashraf
Publikováno v:
IEEE Access, Vol 11, Pp 136091-136108 (2023)
Many stakeholders including students, teachers, and educational institutions, benefit from accurately predicting student performance and facilitating data-driven policies. In this field, providing users with accurate and understandable predictions is
Externí odkaz:
https://doaj.org/article/042f165f6d2743f1a1c46c504ee7ddbd
Publikováno v:
IEEE Access, Vol 11, Pp 18960-18971 (2023)
Learning management systems (LMSs) have been used massively due to the growing utilization of distance learning. This advancement has led to increased educational data that can be analyzed to improve the quality of the learning process. Learning anal
Externí odkaz:
https://doaj.org/article/9028c40519a143549984c88b88dbb351
Publikováno v:
Cybernetics and Information Technologies, Vol 22, Iss 1, Pp 117-133 (2022)
The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic
Externí odkaz:
https://doaj.org/article/74148abb3ec0484fbfce66850df7b4e5
Publikováno v:
International Journal of Educational Technology in Higher Education, Vol 18, Iss 1, Pp 1-18 (2021)
Abstract Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even mo
Externí odkaz:
https://doaj.org/article/e89539dd763a4f558d88492d3563f365