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This study addresses the issue of curiosity‐driven inquiry learning by examining the interplay among students’ inquiry‐related curiosity, laboratory engagement, and inquiry abilities and investigates how their learning experiences would associate with such interplay. We employed structural equation modeling to analyze data collected from 920 eighth graders and 1,090 eleventh graders, including their performances on a multimedia‐based assessment of scientific inquiry abilities and their responses to items in an online questionnaire. The results revealed that, for students from both grades, inquiry‐related curiosity was associated with their inquiry abilities, and that the association was mediated by their inquiry‐related laboratory engagement. Furthermore, the analyses showed that both formal and informal experiences were associated with the laboratory engagement of students from both grades through curiosity. Yet, the results indicated that, although the roles of the two types of experiences varied in the associations, formal science experience had stronger associations with students' inquiry abilities than informal science experience for both graders. Taken together, this study supports the importance of having curiosity‐driven engagement and suggests that the science education community should collaboratively offer secondary school students such learning opportunities in formal and informal science learning settings.

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four master's students, and four PhD students in atmospheric sciences as participants. Participants' reasoning practices and understanding of multivariable causality were examined using semistructured interviews and recordings of their computer activities. Analyses show that participants with high expertise tended to take a mechanism approach to predict and identify relationships, focused more on multivariable relationships, and purposefully selected and tested variables. The findings also indicate that understanding about multiple causality involved recognition and identification of the integration rules of multiple effects and the attributes of variables (e.g., interactive and reciprocal) and relationships (e.g., direction and feedback loop). Additionally, this study suggests an interaction between participants' reasoning practices and their understanding of multivariable causality; participants' understanding about the integration rules and the attributes could initiate reasoning practices, and by the enactment of practices, the rules and attributes were confirmed and examined. This study provides insight into the nature of multivariable reasoning and the design of computer-based modeling tools. C 2013 Wiley Periodicals, Inc. Sci Ed 97:337-366,

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