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This study investigated gender-related differences in the efficacy-expectation/performance discrepancies of high school students across four subject matter domains. Participants (N = 131) completed American College Testing (ACT) assessments in English, mathematics, reading, and science. Additionally, prior to each domain-specific test, students rated their perceived self-efficacy expectation for successful performance on that test. As anticipated, most students reporting greater efficacy expectation also tended to perform at higher levels, particularly in mathematics. Unexpectedly, the majority of student efficacy/performance discrepancy scores fell outside the congruent range in all content areas. Approximately equal numbers of male and female students inaccurately estimated their performance capabilities, with most noncongruent students tending to fall into either the extreme overestimation/underestimation categories. Implications are discussed regarding the application of these findings to refining educational methods, and enhancing self-motivation in educational settings.  相似文献   
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In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics domains, in which many students struggle and where the United States needs skilled workers. This article lays out some issues important for framing research on this topic and provides a review of some existing work with similar goals on engagement in science learning. Specifically, here we seek to help better concretize engagement, a fuzzy construct, by operationalizing and detecting (i.e., identifying using a computational method) disengaged behaviors that are antithetical to engagement. We, in turn, describe our real-time detector (i.e., machine learned model) of disengaged behavior and how it was developed. Last, we address our ongoing research on how our detector of disengaged behavior will be used to intervene in real time to better support students' science inquiry learning in Inq-ITS (Inquiry-Intelligent Tutoring System; Gobert, Sao Pedro, Baker, Toto, & Montalvo, 2012; Gobert, Sao Pedro, Raziuddin, & Baker, 2013).  相似文献   
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Since the initiation of performance funding in Tennessee in the late 1970s, approximately 30 states have, at some point, attempted a funding model that includes performance on a set of indicators. The purpose of the present study was to capture the current status of performance funding in public statewide community college systems and to assess which performance indicators were collected at the state level. Data were obtained through the 2012 Survey of Finance and Access Issues conducted by the Education Policy Center at the University of Alabama. The survey was administered to the National Council of State Directors of Community Colleges, and 50 responses were obtained from 49 states. Not every survey respondent answered each survey item. Findings showed that 19 states used performance funding for community colleges, with six of those states allocating at least 10% of state funds based on performance. An analysis of indicators captured at the state level (not exclusively for performance funding) showed a decline of emphasis on process indicators and greater emphasis on outputs. The performance-based distribution of base funding and the inclusion of output measures that capture intermediate indicators of success are consistent with the principles of Performance Funding 2.0, a new form of performance funding recently discussed in the literature. Future research should include tracking the current findings over time and expanding the existing literature on whether performance funding influences outcomes.  相似文献   
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Specializations: cognitive processes in science learning and teaching.  相似文献   
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