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Collier-Meek, Melissa A.; Sanetti, Lisa M.; Fallon, Lindsay; Chafouleas, Sandra – Assessment for Effective Intervention, 2020
Treatment fidelity data are critical to evaluate intervention effectiveness, yet there are only general guidelines regarding treatment fidelity measurement. Initial investigations have found treatment fidelity data collected via direct observation to be more reliable than data collected via permanent product or self-report. However, the comparison…
Descriptors: Intervention, Evaluation Methods, Fidelity, Observation
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Hall, Jori N.; Ryan, Katherine E. – Qualitative Inquiry, 2011
This article discusses the importance of mixed-methods research, in particular the value of qualitatively driven mixed-methods research for quantitatively driven domains like educational accountability. The article demonstrates the merits of qualitative thinking by describing a mixed-methods study that focuses on a middle school's system of…
Descriptors: Methods Research, Evaluation Methods, Accountability, Case Studies
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Lin, Wan-Ying; Cheong, Pauline Hope; Kim, Yong-Chan; Jung, Joo-Young – Journal of Adolescent Research, 2010
Asian youths are embracing communication technologies at a burgeoning rate, yet interesting differences in Internet access and use exist among this younger generation. Our empirical investigation provides a rich and comparative look into what Asian youths do online, with an emphasis on an understudied area: their civic uses of new media. Data…
Descriptors: Citizenship, Games, Foreign Countries, Internet
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Bark, Katie; Stenberg, Molly; Sutherland, Shelly; Hayes, Dayle – Journal of Child Nutrition & Management, 2010
Purpose/Objectives: The purpose of the "Montana Recess Before Lunch Survey" was to explore benefits, challenges, and factors associated with successful implementation of Recess Before Lunch (RBL), from the perspective of school principals. Methods: An online written questionnaire was distributed to all (N = 661) Montana elementary and…
Descriptors: Student Behavior, Nutrition, Wellness, Principals
LeTendre, Brenda Guenther; Lipka, Richard P. – 1999
This guide gives middle school teachers and principals the tools they need to get answers to their questions about program evaluation. It shows how to use the information to work more effectively and efficiently and explains how to use program evaluation techniques to make sure that the approaches selected really solve the problems of interest.…
Descriptors: Data Analysis, Data Collection, Evaluation Methods, Middle Schools
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
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Cooksy, Leslie J.; Gill, Paige; Kelly, P. Adam – Evaluation and Program Planning, 2001
Describes the use of the program logic model as an integrative framework in the multimethod evaluation of a middle school curriculum delivery program. Shows how the logic model was used to focus data collection, organize data, and interpret the data in an integrative framework. Focuses on the area of computer skills to illustrate the usefulness of…
Descriptors: Computer Literacy, Curriculum, Data Collection, Evaluation Methods
Brandt, Ronald S., Ed. – 1981
Evaluation of a middle school humanities program is the focus of this report. It explains how to identify information needs and set priorities, how to obtain information from a variety of sources, and what to do with the data collected in terms of formulating recommendations for the school board. The variety of evaluation approaches presented are…
Descriptors: Curriculum Evaluation, Data Collection, Evaluation Methods, Humanities
Borko, Hilda; Kuffner, Karin L.; Arnold, Suzanne C.; Creighton, Laura; Stecher, Brian M.; Martinez, Felipe; Barnes, Dionne; Gilbert, Mary Lou – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2007
It is important to be able to describe instructional practices accurately in order to support research on "what works" in education and professional development as a basis for efforts to improve practice. This report describes a project to develop procedures for characterizing classroom practices in mathematics and science on the …
Descriptors: Teaching Methods, Scoring, Field Studies, Educational Change
Ingels, Steven J.; And Others – 1989
Nonresponse issues are investigated for the base year (1988) survey of the United States Department of Education's National Education Longitudinal Study of 1988 (NELS:88), a national probability sample of middle schools and eighth-grade students in the spring of 1988. The total eighth-grade enrollment for the NELS:88 sample of schools was 203,002;…
Descriptors: Data Collection, Educational Assessment, Elementary Secondary Education, Estimation (Mathematics)
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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