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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
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Guggemos, Josef – International Association for Development of the Information Society, 2023
Computational thinking (CT) is an important 21st-century skill. This paper aims at investigating predictors of CT self-efficacy among high-school students. The hypothesized predictors are grouped into three areas: (1) student characteristics, (2) home environment, and (3) learning opportunities. CT self-efficacy is measured with the Computational…
Descriptors: Computation, Thinking Skills, Self Efficacy, 21st Century Skills
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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
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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
Wiggins, Afi Y.; Stelling, Laura – Online Submission, 2015
This supplemental report provides technical documentation for the full report (published separately) concerning the 63% of AISD's Class of 2013 graduates who enrolled in a postsecondary institution the year after high school, and 74% of Class of 2012 graduates persisted in college for a second year. [For the full report, see ED626498.]
Descriptors: Postsecondary Education, College Attendance, Enrollment Trends, High School Graduates