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ABSTRACT:Interest is a powerful motivator; nonetheless, science educators often lack the necessary information to make use of the power of student-specific interests in the reform process of science curricula. This study suggests a novel methodology, which might be helpful in identifying such interests-using children's self-generated questions as an indication of their scientific interests. In this research, children's interests were measured by analyzing 1555 science-related questions submitted to an international Ask-A-Scientist Internet site. The analysis indicated that the popularity of certain topics varies with age and gender. Significant differences were found between children's spontaneous (intrinsically motivated) and school-related (extrinsically motivated) interests. Surprisingly, girls contributed most of the questions to the sample; however, the number of American girls dropped upon entering senior high school. We also found significant differences between girls' and boys' interests, with girls generally preferring biological topics. The two genders kept to their stereotypic fields of interest, in both their school-related and spontaneous questions. Children's science interests, as inferred from questions to Web sites, could ultimately inform classroom science teaching. This methodology extends the context in which children's interests can be investigated. Century, 2000) states that "we are failing to capture the interest of youth for scientific and mathematical ideas." Indeed, many students find standard science curricula largely out of touch with their personal interests, a factor which contributes to the low number of students pursuing advanced science and mathematics courses in high school, and going on to choose scientific careers (Millar & Osborne, 1998). Adolescents' decisions about the contents and directions of their educational training have been found to be influenced to a high degree by the topic-related interests they developed in the preceding years (Krapp, 2000).Organizations, including the National Research Council (1996) and the American Association for the Advancement of Science (1993), have proposed that science curricula taught at a secondary-school level should provide a common basis of knowledge while addressing the particular needs and interests of students. However, educators lack the necessary information and tools to guide modifications that could make use of the power of student-specific interests in improving those students' individualized learning and competency in scientific subjects.The issue of students' interests may also be viewed in the context of the pupil's voice in the education movement (Burke & Grosvenor, 2003;Economic and Social Research Council, 2004;Mirta, 2004;Whitehead & Clough, 2004). Until recently, the pupil's voice had been marginalized or neglected by educational researchers. The student was regarded as an object of study but not as someone who could make an informed judgment on what should be taught in school science courses (Jenkins & Nelson, 20...

The rapid growth in public communication of science and technology has led to a highly diverse and large number of training programs. Using a learning-centered approach, we ask: What are the learning goals of science communication training? As the science communication field matures, a comprehensive set of learning goals for future trainings will draw fully from the range of fields that contribute to it. Learning goals provide a framework for deciding what to count as success and how to gather evidence of learning.Based on the six strands of learning developed for "learning science in informal environments", we built a conceptually-coherent definition of science communication learning that addresses affective issues, content knowledge, methods, reflection, participation, and identity.We then reviewed dozens of research articles describing science communication training for scientists, identifying both explicit and implicit learning goals. Classifying them with our conceptual definition, we identified gaps in the outcomes commonly used for training programs; these gaps appeared especially in the areas of affective learning and identity formation.We do not expect any one program would attempt to achieve all learning goals. Different courses might be tailored differently for training scientists who remain in science, who wish to become journalists, who wish to work for museums, etc. But we believe that conceptual coherence can help course designers identify important goals. Creating a common language will increase the ability to compare outcomes across courses and programs, identifying approaches that best fit particular education, training, and communication contexts.

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Scientists are required to communicate science and research not only to other experts in the field, but also to scientists and experts from other fields, as well as to the public and policymakers. One fundamental suggestion when communicating with non-experts is to avoid professional jargon. However, because they are trained to speak with highly specialized language, avoiding jargon is difficult for scientists, and there is no standard to guide scientists in adjusting their messages. In this research project, we present the development and validation of the data produced by an up-to-date, scientist-friendly program for identifying jargon in popular written texts, based on a corpus of over 90 million words published in the BBC site during the years 2012–2015. The validation of results by the jargon identifier, the De-jargonizer, involved three mini studies: (1) comparison and correlation with existing frequency word lists in the literature; (2) a comparison with previous research on spoken language jargon use in TED transcripts of non-science lectures, TED transcripts of science lectures and transcripts of academic science lectures; and (3) a test of 5,000 pairs of published research abstracts and lay reader summaries describing the same article from the journals PLOS Computational Biology and PLOS Genetics. Validation procedures showed that the data classification of the De-jargonizer significantly correlates with existing frequency word lists, replicates similar jargon differences in previous studies on scientific versus general lectures, and identifies significant differences in jargon use between abstracts and lay summaries. As expected, more jargon was found in the academic abstracts than lay summaries; however, the percentage of jargon in the lay summaries exceeded the amount recommended for the public to understand the text. Thus, the De-jargonizer can help scientists identify problematic jargon when communicating science to non-experts, and be implemented by science communication instructors when evaluating the effectiveness and jargon use of participants in science communication workshops and programs.