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Coen, Thomas; Nichols-Barrer, Ira; Gleason, Philip – Mathematica, 2019
In this report, the authors present the results of a long-term tracking study that follows 1,177 students who applied to enter 1 of 13 oversubscribed Knowledge Is Power Program (KIPP) middle schools through a 5th or 6th grade admissions lottery in 2008 or 2009. Those students are now old enough to have attended college for at least two years. This…
Descriptors: Middle Schools, Program Effectiveness, College Attendance, Academic Persistence
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Toluk-Ucar, Zulbiye; Bozkus, Figen – International Journal for Mathematics Teaching and Learning, 2018
The purpose of this study was to investigate how prospective elementary school mathematics teachers and elementary school students' ability to distinguish proportional situations from nonproportional situations differ or are alike. Participants of the study were 319 prospective primary and middle school teachers and 320 elementary school students…
Descriptors: Elementary School Students, Preservice Teachers, Mathematics Instruction, Elementary Education
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Park, Sanghoon; Gentry, Vickie – Journal of Service-Learning in Higher Education, 2017
The Collaborative Multimedia Service-Learning (CMSL) model, based on the "learning by teaching" approach, was created in an effort to design a pedagogical intervention to promote pre-service teachers' multimedia design/development skills. This paper reports on the development of the CMSL and presents the findings of the implementation of…
Descriptors: Service Learning, Multimedia Instruction, Preservice Teachers, Preservice Teacher Education
Allensworth, Elaine M.; Gwynne, Julia A.; Moore, Paul; de La Torre, Marisa – University of Chicago Consortium on Chicago School Research, 2014
Preparation for college and careers begins when students are young, yet, it can be difficult for middle grade educators to know how best to prepare these students for future success. Middle grade practitioners need to know what to pay attention to and who needs additional support. Without knowing how to identify students who are on-track for high…
Descriptors: Middle Schools, Urban Schools, College Preparation, At Risk Students
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Fueyo, Vivian; Roy, George; Vahey, Phillip – Educational Renaissance, 2013
By leveraging the strengths and commitments of each of the partners, a university, a private nonprofit, and a middle-sized urban school district, collaborated to impact student learning of key concepts in middle-grade mathematics and to change mathematics teaching. The project targeted middle grades mathematics because success in it is the…
Descriptors: Educational Technology, Technology Uses in Education, Mathematics Instruction, Teaching Methods
Houston Independent School District, 2013
The Apollo 20 project was launched during the 2010-2011 school year to accelerate Houston Independent School District's (HISD's) efforts to improve student performance in every school and close the achievement gap districtwide. This partnership with EdLabs at Harvard University incorporates best practices from successful public and charter schools…
Descriptors: School Districts, Academic Achievement, Achievement Gap, Partnerships in Education
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Carmo, Mafalda, Ed. – Online Submission, 2017
This book contains a compilation of papers presented at the International Conference on Education and New Developments (END 2017), organized by the World Institute for Advanced Research and Science (W.I.A.R.S.). Education, in our contemporary world, is a right since we are born. Every experience has a formative effect on the constitution of the…
Descriptors: Educational Quality, Models, Vocational Education, Outcomes of Education
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection