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991.
Elizabeth P. Beggrow Minsu Ha Ross H. Nehm Dennis Pearl William J. Boone 《Journal of Science Education and Technology》2014,23(1):160-182
The landscape of science education is being transformed by the new Framework for Science Education (National Research Council, A framework for K-12 science education: practices, crosscutting concepts, and core ideas. The National Academies Press, Washington, DC, 2012), which emphasizes the centrality of scientific practices—such as explanation, argumentation, and communication—in science teaching, learning, and assessment. A major challenge facing the field of science education is developing assessment tools that are capable of validly and efficiently evaluating these practices. Our study examined the efficacy of a free, open-source machine-learning tool for evaluating the quality of students’ written explanations of the causes of evolutionary change relative to three other approaches: (1) human-scored written explanations, (2) a multiple-choice test, and (3) clinical oral interviews. A large sample of undergraduates (n = 104) exposed to varying amounts of evolution content completed all three assessments: a clinical oral interview, a written open-response assessment, and a multiple-choice test. Rasch analysis was used to compute linear person measures and linear item measures on a single logit scale. We found that the multiple-choice test displayed poor person and item fit (mean square outfit >1.3), while both oral interview measures and computer-generated written response measures exhibited acceptable fit (average mean square outfit for interview: person 0.97, item 0.97; computer: person 1.03, item 1.06). Multiple-choice test measures were more weakly associated with interview measures (r = 0.35) than the computer-scored explanation measures (r = 0.63). Overall, Rasch analysis indicated that computer-scored written explanation measures (1) have the strongest correspondence to oral interview measures; (2) are capable of capturing students’ normative scientific and naive ideas as accurately as human-scored explanations, and (3) more validly detect understanding than the multiple-choice assessment. These findings demonstrate the great potential of machine-learning tools for assessing key scientific practices highlighted in the new Framework for Science Education. 相似文献
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Introduction 总被引:1,自引:0,他引:1
Mary Elizabeth McCormac 《Journal of Career Development》1988,14(3):141-142
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Patricia F. Vadasy Elizabeth A. Sanders Julia A. Peyton Joseph R. Jenkins 《Learning disabilities research & practice》2002,17(4):227-241
In this article we report data from a longitudinal study of one–to–one tutoring for students at risk for reading disabilities. Participants were at–risk students who received phonics–based tutoring in first grade, students who were tutored in comprehension skills in second grade, and students tutored in both grades 1 and 2. At second–grade posttest, there were significant differences in word identification and word attack between students who were tutored in first grade only compared to students who were also tutored in second grade, favoring students who were tutored in first grade only. Overall, there were no advantages to a second year of tutoring. For students tutored in second grade only, there were no differences at second–grade posttest compared to controls. Schools may have selected students who did not respond to first–grade tutoring for continued tutoring in second grade. Findings are discussed in light of decisions schools make when using tutors to supplement reading instruction for students with reading difficulties. 相似文献
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