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Cook, Michael; Ross, Steven M. – Center for Research and Reform in Education, 2022
The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction that met Curriculum Associates' recommended usage levels on mathematics achievement, as measured by the Massachusetts Comprehensive Assessment System (MCAS) mathematics assessment. This study compared mathematics achievement growth of students who used…
Descriptors: Mathematics Achievement, Mathematics Instruction, Program Evaluation, Individualized Instruction
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Cook, Michael; Ross, Steven M. – Center for Research and Reform in Education, 2022
The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction that met Curriculum Associates' recommended usage levels on ELA achievement, as measured by the Massachusetts Comprehensive Assessment System (MCAS) ELA assessment. This study compared the ELA achievement growth in the 2020-21 school year of students who…
Descriptors: English, Language Arts, Computer Assisted Instruction, Computer Assisted Testing
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Conradty, Cathérine; Bogner, Franz X. – Education Sciences, 2016
Computers are considered innovative in classrooms, raising expectations of increased cognitive learning outcomes or motivation with effects on Deeper Learning (DL). The "new medium", however, may cause cognitive overloads. Combined with gender-related variations in ability, self-efficacy or self-confidence, computers may even diminish…
Descriptors: Hypermedia, Technology Uses in Education, Educational Technology, Student Motivation
Gobert, Janice D.; Koedinger, Kenneth R. – Society for Research on Educational Effectiveness, 2011
The National frameworks for science emphasize inquiry skills (NRC, 1996), however, in typical classroom practice, science learning often focuses on rote learning in part because science process skills are difficult to assess (Fadel, Honey, & Pasnick, 2007) and rote knowledge is prioritized on high-stakes tests. Short answer assessments of…
Descriptors: Performance Based Assessment, Predictive Validity, High Stakes Tests, Rote Learning