The relationship of STEM attitudes and career interest

E Wiebe, A Unfried, M Faber - EURASIA Journal of …, 2018 - digitalcommons.csumb.edu
This study examines the relationships between attitudes toward all core STEM subjects and
interest in STEM careers among 4th through 12th grade US students through the …

Predicting university student graduation using academic performance and machine learning: a systematic literature review

LR Pelima, Y Sukmana, Y Rosmansyah - IEEE Access, 2024 - ieeexplore.ieee.org
Predicting university student graduation is a beneficial tool for both students and institutions.
With the help of this predictive capacity, students may make well-informed decisions about …

University students' views regarding gender in STEM studies: Design and validation of an instrument

S Verdugo-Castro, MC Sánchez-Gómez… - Education and …, 2022 - Springer
Differences in the representation of diversity in higher education, emphasising the gender
gap in some areas, are issues addressed from different research domains. Socially, gender …

A review on machine learning based student's academic performance prediction systems

R Katarya, J Gaba, A Garg… - … Conference on Artificial …, 2021 - ieeexplore.ieee.org
Prediction of academic performance of students beforehand provides scope to universities
to lower their dropout rate and help the students in improving their performance. In this field …

[HTML][HTML] Gendered patterns in students' motivation profiles regarding iSTEM and STEM test scores: A cluster analysis

S Hermans, M Gijsen, T Mombaers… - International Journal of …, 2022 - Springer
Promoting and improving STEM education is being driven by economic concerns as modern
economies have a rising demand for qualified researchers, technicians, and other STEM …

Beyond the leaky pipeline: Developmental pathways that lead college students to join or return to STEM majors

J Wu, D Uttal - Journal of Research in STEM Education, 2020 - j-stem.net
STEM education researchers often invoke the “Leaky Pipeline” metaphor (National
Research Council, 1986) when explaining why so many students do not persist in STEM …

Contributions of machine learning models towards student academic performance prediction: a systematic review

P Balaji, S Alelyani, A Qahmash, M Mohana - Applied Sciences, 2021 - mdpi.com
Machine learning is emerging nowadays as an important tool for decision support in many
areas of research. In the field of education, both educational organizations and students are …

Students' interest in STEM education

S Makhmasi, R Zaki, H Barada… - Proceedings of the …, 2012 - ieeexplore.ieee.org
In this paper we study the interest of students in the United Arab Emirates (UAE) from grade
9 to 12 in Science, Technology, Engineering, and Mathematics (STEM). Surveys were …

Press: Predicting student success early in cs1. a pilot international replication and generalization study

K Quille, S Nam Liao, E Costelloe, K Nolan… - Proceedings of the 27th …, 2022 - dl.acm.org
This work piloted an international replication and generalization study on an existing
prediction model called PreSS. PreSS has been developed and validated over nearly two …

[PDF][PDF] Operation STEM: Increasing success and improving retention among mathematically underprepared students in STEM

SD Carver, J Van Sickle, JP Holcomb… - Journal of STEM …, 2017 - academia.edu
Abstract In 2012, Cleveland State University implemented a comprehensive program, called
Operation STEM (Op-STEM), funded by two National Science Foundation grants, federal …