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Predicting how students will perform on standardized tests is relevant to all educators. This work examines the relationship of students' performance on teacher-edited benchmark assessments administered to their performance on the state's end-of-course test. Teachers have information on student achievement within specific topics based on tests, quizzes, homework, and other assignments. However, the best way to use this information in predicting high-stakes testing is unknown. Providing teachers with guidance on how to use this information would be extremely valuable. This research determines…mehr

Produktbeschreibung
Predicting how students will perform on standardized tests is relevant to all educators. This work examines the relationship of students' performance on teacher-edited benchmark assessments administered to their performance on the state's end-of-course test. Teachers have information on student achievement within specific topics based on tests, quizzes, homework, and other assignments. However, the best way to use this information in predicting high-stakes testing is unknown. Providing teachers with guidance on how to use this information would be extremely valuable. This research determines how well the data from periodically-administered tests may predict scores on the state's end-of-course test. The school implemented an intervention process that involves frequent assessment of the skills being taught. Students were taught and re-taught, tested and re-tested until they mastered the curriculum. It is my hope that the research reported here will help decision-makers on educationwith evidence-based guidance on how to use student data to drive instructions.
Autorenporträt
Roland Damasco Dante was born in the Philippines and obtained his Bachelor¿s and Master¿s degree, both in his home country. He is now on his 20th year in the teaching profession. He completed his second Master on Natural Sciences at Louisiana State University, Baton Rouge Louisiana. He is currently the Math Department Head of his school.