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Ally Shay Thomas 《Educational Measurement》2018,37(2):4-4

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The Grand Challenges Discourse: Transforming Identity Work in Science and Science Policy     

David Kaldewey 《Minerva》2018,56(2):161-182

This article analyzes the concept of “grand challenges” as part of a shift in how scientists and policymakers frame and communicate their respective agendas. The history of the grand challenges discourse helps to understand how identity work in science and science policy has been transformed in recent decades. Furthermore, the question is raised whether this discourse is only an indicator, or also a factor in this transformation. Building on conceptual history and historical semantics, the two parts of the article reconstruct two discursive shifts. First, the observation that in scientific communication references to “problems” are increasingly substituted by references to “challenges” indicates a broader cultural trend of how attitudes towards what is problematic have shifted in the last decades. Second, as the grand challenges discourse is rooted in the sphere of sports and competition, it introduces a specific new set of societal values and practices into the spheres of science and technology. The article concludes that this process can be characterized as the sportification of science, which contributes to self-mobilization and, ultimately, to self-optimization of the participating scientists, engineers, and policymakers.  相似文献   

Progress Is Not an Illusion     

Oren Harman 《Science & Education》2018,27(1-2):219-224

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Letters     

Seth Forman 《Academic Questions》2018,31(3):251-251

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Kuhn’s Image of Science     

William Rehg 《Metascience》2018,27(2):213-215

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Disconnect: Kentucky and the Political Ideology of its Public Universities     

Ben Foster 《Academic Questions》2018,31(3):313-321

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The Evolution of U.S. Museums and Science Centers as Informal Learning Environments     

Renee M. Clary 《Science & Education》2018,27(3-4):371-377

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The Historical Heart of Atmospheric Science     

Anthony J. Sadar 《Science & Education》2018,27(1-2):233-235

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What motivates enrolment in programming MOOCs?     

Piret Luik  Reelika Suviste  Marina Lepp  Tauno Palts  Eno Tõnisson  Merilin Säde  Kaspar Papli 《British journal of educational technology : journal of the Council for Educational Technology》2019,50(1):153-165

Learners who enrol in massive open online courses (MOOCs) have different backgrounds and tend to have different motivations than learners in traditional courses. Based on value-expectancy theory, an instrument was developed to measure motivation for enrolling in a programming MOOC. A study with 1229 adult participants in Estonian-language programming course “About Programming” was conducted to validate the instrument. Results of confirmatory factor analysis validated the 7-factor scale named factors influencing enrolment in MOOC (FIEM). FIEM comprises three factors of expectancies, three factors of values and one factor of social influence. The highest and lowest rated motivational factors influencing enrolment in programming MOOC are discussed in the paper. Interest in and expectations for the course, personal suitability of distance learning and suitability for family and work are the highest-rated motivational factors for those who enrol in MOOC. Usefulness related to own children, social influence and usefulness to related to certification were the lowest rated. The results of this study can be useful for designers of programming MOOCs and the developed scale might be used in future studies.  相似文献   

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com     

Wenchao Ma  Jimmy de la Torre 《Educational Measurement》2019,38(2):114-115

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.  相似文献