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Round, Jennifer E.; Campbell, A. Malcolm – CBE - Life Sciences Education, 2013
The ability to interpret experimental data is essential to understanding and participating in the process of scientific discovery. Reading primary research articles can be a frustrating experience for undergraduate biology students because they have very little experience interpreting data. To enhance their data interpretation skills, students…
Descriptors: Biology, Undergraduate Students, Reading Assignments, Data Interpretation
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Gee, Kevin A. – American Journal of Evaluation, 2014
The growth in the availability of longitudinal data--data collected over time on the same individuals--as part of program evaluations has opened up exciting possibilities for evaluators to ask more nuanced questions about how individuals' outcomes change over time. However, in order to leverage longitudinal data to glean these important insights,…
Descriptors: Longitudinal Studies, Data Analysis, Statistical Studies, Program Evaluation
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Rademaker, Linnea L.; Grace, Elizabeth J.; Curda, Stephen K. – Qualitative Report, 2012
As diverse members of a college of education evaluation committee one of our charges is to support faculty as we document and improve our teaching. Our committee asked faculty to respond to three qualitative questions, documenting ways in which interdepartmental and cross-department conversations are used to promote reflective thinking about our…
Descriptors: Computer Assisted Testing, Data Analysis, Qualitative Research, Courseware
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Barton, Erin E.; Reichow, Brian – Journal of Early Intervention, 2012
The interpretation of single-case data requires systematic visual analysis across and within conditions. Graphs are a vital component for analyzing and communicating single-case design data and a necessary tool for applied researchers and practitioners. Several articles have been published with task analyses for graphing data with the new versions…
Descriptors: Computer Literacy, Guidelines, Graphs, Computer Software
Pascopella, Angela – District Administration, 2012
Predicting the future is now in the hands of K12 administrators. While for years districts have collected thousands of pieces of student data, educators have been using them only for data-driven decision-making or formative assessments, which give a "rear-view" perspective only. Now, using predictive analysis--the pulling together of data over…
Descriptors: Expertise, Prediction, Decision Making, Data
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Zoanetti, Nathan – Australasian Journal of Educational Technology, 2010
This article presents key steps in the design and analysis of a computer based problem-solving assessment featuring interactive tasks. The purpose of the assessment is to support targeted instruction for students by diagnosing strengths and weaknesses at different stages of problem-solving. The first focus of this article is the task piloting…
Descriptors: Computer Assisted Testing, Computer Uses in Education, Problem Solving, Evaluation Methods
Callison, Daniel; Haycock, Gloria – Educational Technology, 1988
Describes a methodology for student involvement in courseware evaluation and for utilizing student reactions in purchasing and implementation decisions. The evaluation forms used in a survey of 2,308 Indiana students are presented, and the results of that survey are discussed. (18 references) (CLB)
Descriptors: Computer Assisted Instruction, Courseware, Data Interpretation, Elementary Secondary Education
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Bellomonte, L.; Sperandeo-Mineo, R. M. – Computers and Education, 1993
Discussion of mathematical modeling, particularly for high school physics curricula, focuses on software that is connected with laboratory work and the inference of mathematical models based on measurements of physical quantities. Fitting procedures are described, and user interface is explained. (Contains nine references.) (LRW)
Descriptors: Computer Assisted Instruction, Courseware, Data Interpretation, High Schools
Smith, Richard Alan – Computing Teacher, 1988
Discusses how to examine and evaluate claims of improved academic performance in advertisements for computer-assisted instruction. Highlights include the proper use of comparison groups; types of statistical analyses; the Hawthorne effect; the interpretation of scores; interpreting graphic presentations; tests of significance; and cost…
Descriptors: Academic Achievement, Achievement Gains, Advertising, Comparative Analysis