Results 21 to 30 of about 29,948 (298)

Forecasting Students Dropout: A UTAD University Study [PDF]

Future Internet, 2022
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades.
Diogo E. Moreira da Silva, Eduardo José Solteiro Pires, Arsénio Reis, Paulo B. de Moura Oliveira, João Barroso 0001   +4 more
openaire   +2 more sources

From participation to dropout [PDF]

, 2010
The academic e-learning practice has to deal with various participation patterns and types of online learners with different support needs. The online instructors are challenged to recognize these and react accordingly.
Neubauer, Katrin, Nistor, Nicolae
core   +1 more source

Predictors of academic efficacy and dropout intention in university students: Can engagement suppress burnout?

PLoS ONE, 2020
In this study we modelled possible causes and consequences of student burnout and engagement on academic efficacy and dropout intention in university students. Further we asked, can student engagement protect against the effects of burnout?
João Marôco, Hugo Assunção, Heidi Harju-Luukkainen, Su-Wei Lin, Pou-Seong Sit, Kwok-Cheung Cheung, Benvindo Maloa, Ivana Stepanović Ilic, Thomas J Smith, Juliana A D B Campos   +9 more
doaj   +1 more source

Effectiveness of the settings-based intervention Shaping the Social on preventing dropout from vocational education: a Danish non-randomized controlled trial

BMC Psychology, 2018
Background Lack of formal education is an important social determinant of health inequality and represents a public health problem. School dropout is particularly common in vocational education; however few prevention programs targeting dropout in the ...
Susan Andersen, Morten Hulvej Rod, Teresa Holmberg, Liselotte Ingholt, Annette Kjær Ersbøll, Janne Schurmann Tolstrup   +5 more
doaj   +1 more source

Enhanced Model for Predicting Student Dropouts in Developing Countries Using Automated Machine Learning Approach: A Case of Tanzanian’s Secondary Schools

Applied Artificial Intelligence, 2022
The Sub-Saharan countries are leading in dropout rates in secondary schools by 37.5% followed by South Asia 15.5% and Middle East 11% in 2018. In Tanzania, student dropouts in secondary schools increased from 3.8% in 2018 to 4.2% in 2019.
Yuda N. Mnyawami, Hellen H. Maziku, Joseph C. Mushi   +2 more
doaj   +1 more source

Professional strategies in upper secondary school dropout management among youth in the Sami areas of Norway: a focus group study

International Journal of Circumpolar Health, 2023
The upper secondary school dropout rate is a challenge in many western countries, and measures have been taken to prevent dropout. The dropout rate in Norway is stable but is the highest in the northernmost counties.
Tone Aashild Dinesen, Bodil H. Blix, Astrid Gramstad   +2 more
doaj   +1 more source

Predicting Student Dropout in Higher Education

CoRR, 2016
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition.
Lovenoor S. Aulck, Nishant Velagapudi, Joshua Blumenstock, Jevin West   +3 more
openaire   +2 more sources

Performing Learning Analytics via Generalised Mixed-Effects Trees

Data, 2021
Nowadays, the importance of educational data mining and learning analytics in higher education institutions is being recognised. The analysis of university careers and of student dropout prediction is one of the most studied topics in the area of ...
Luca Fontana, Chiara Masci, Francesca Ieva, Anna Maria Paganoni   +3 more
doaj   +1 more source

Student Dropout Prediction

International Journal of Advanced Research in Science, Communication and Technology
Student dropout poses a major challenge to educational institutions, affecting academic performance and institutional reputation. This study applies machine learning techniques to predict at-risk students using data from the Department of Computer Science, University of Benin (2016–2020), with 906 records analyzed. Six classifiers—Naive Bayes, Logistic
null Anjana S, null Dr. V. Vijayakumar
openaire   +2 more sources

The minimum dropout age and student victimization

Economics of Education Review, 2012
Abstract Over the years, the minimum dropout age has been raised to 18 in 21 states. Although these policy changes are promoted for their educational benefits, they have been shown to reduce crimes committed by youths in the affected age groups. However, an unintended consequence of increasing the minimum dropout age could be the displacement of ...
Anderson, D. Mark, Hansen, Benjamin, Walker, Mary Beth   +2 more
openaire   +2 more sources