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Munise Seçkin Kapucu; I?brahim Özcan; Hülya Özcan; Ahmet Aypay – International Journal of Technology in Education and Science, 2024
Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…
Descriptors: Secondary School Students, Science Achievement, Artificial Intelligence, Foreign Countries
Yvonne Allsop; Arianna Black; Eric M. Anderman – Sex Education: Sexuality, Society and Learning, 2024
Sexual health education should meet the needs of all students. One strategy educators can use to ensure instruction meets students' needs is to encourage the submission of anonymous questions, allowing students to gain information without fear of instructor or peer reactions. We investigated anonymous questions submitted by middle school…
Descriptors: Sex Education, Sexuality, Middle School Students, Health Education
Gerald Tindal; Joseph F. T. Nese – Behavioral Research and Teaching, 2024
We present two types of validity evidence to support inferences and decisions about use of easyCBMs in relation to state testing programs. The first type involves the use of Benchmarks in reading to use in making predictions of performance on the Smarter Balanced (SB) test. These predictions can be made both well in advance (several months) or…
Descriptors: Classification, Accuracy, Validity, Criteria
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