NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 7 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Hikmet Sevgin – International Journal of Assessment Tools in Education, 2023
This study aims to conduct a comparative study of Bagging and Boosting algorithms among ensemble methods and to compare the classification performance of TreeNet and Random Forest methods using these algorithms on the data extracted from ABIDE application in education. The main factor in choosing them for analyses is that they are Ensemble methods…
Descriptors: Algorithms, Mathematics Education, Classification, Mathematics Achievement
Peer reviewed Peer reviewed
Direct linkDirect link
Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Karen C. Fuson; Shannon Kiebler; Robyn Decker – Mathematics Teacher: Learning and Teaching PK-12, 2024
The authors have found that having students learn accessible standard algorithms by explaining them using mathematics drawings increases students' sense of place--value numbers and enables students to articulate their understanding of what is actually happening with the numbers and why. In this article, they will discuss three standard algorithms…
Descriptors: Mathematics Instruction, Multilingualism, Teaching Methods, Teacher Student Relationship
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Hark Söylemez, Nesrin – Shanlax International Journal of Education, 2023
This study aims to determine the independent variables that have a significant effect on the level of students' adaptation to online education and their order of importance. Relational screening model was used in the study. Adaptability Level in Online Education dataset provided by Kaggle repository constitutes the main data source for this study.…
Descriptors: Student Adjustment, Online Courses, COVID-19, Pandemics
Peer reviewed Peer reviewed
Direct linkDirect link
Marie-Monique Schaper; Mariana Aki Tamashiro; Rachel Charlotte Smith; Ole Sejer Iversen – ACM Transactions on Computing Education, 2025
As emerging technologies are rapidly advancing as part of our societies and everyday life, it is crucial to include and empower all students in learning about computing and advanced technologies. These include technical capabilities of algorithms, such as the use of AI, that enable novel interactions between humans and their environment and give…
Descriptors: Inclusion, Artificial Intelligence, Student Empowerment, Algorithms
Peer reviewed Peer reviewed
Direct linkDirect link
Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students