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The collision of the Sino-Korean and Yangtze blocks to form a significant part of China is recorded in the Qinling, Tongbai, and Dabie Mountains. Radiometric ages of the ultrahigh-pressure metamorphic rocks in the South Qinling orogenic belt suggest that subduction and collision took place during the Triassic Period. Our new 40Ar/39Ar geochronology of units in the North Qinling orogenic belt confirms that high-grade metamorphism and deformation took place also during the Silurian-Devonian and Carboniferous Periods. These results imply that the amalgamation of eastern China was a multistage process extending over at least 200 m.y.

The ocean response to wintertime sea ice retreat is investigated in the coupled climate model HiGEM. We focus on the marginal ice zone and adjacent waters of the Nordic seas, where the air–sea temperature difference can be large during periods of off-ice winds promoting high heat flux events. Both control and transient climate model ensembles are examined, which allows us to isolate the ocean response due to sea ice retreat from the response due to climate change. As the wintertime sea ice edge retreats toward the Greenland coastline, it exposes waters that were previously covered by ice, which enhances turbulent heat loss and mechanical mixing, leading to a greater loss of buoyancy and deeper vertical mixing in this location. However, under global warming, the buoyancy loss is inhibited as the atmosphere warms more rapidly than the ocean, which reduces the air–sea temperature difference. This occurs most prominently farther away from the retreating ice edge, over the Greenland Sea Gyre. Over the gyre the upper ocean also warms significantly, resulting in a more stratified water column and, as a consequence, a reduction in the depth of convective mixing. In contrast, closer to the coast the effect of global warming is overshadowed by the effect of the sea ice retreat, leading to significant changes in ocean temperature and salinity in the vicinity of the marginal ice zone. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been increasingly employed in science assessment to facilitate automatic scoring efforts, although with varying degrees of success (i.e., magnitudes of machine-human score agreements [MHAs]). Little work has empirically examined the factors that impact MHA disparities in this growing field, thus constraining the improvement of machine scoring capacity and its wide applications in science education. We performed a meta-analysis of 110 studies of MHAs in order to identify the factors most strongly contributing to scoring success (i.e., high Cohen's kappa [κ]). We empirically examined six factors proposed as contributors to MHA magnitudes: algorithm, subject domain, assessment format, construct, school level, and machine supervision type. Our analyses of 110 MHAs revealed substantial heterogeneity in κ (m e a n =.64; range =.09-.97 , taking weights into consideration). Using three-level random-effects modeling, MHA score heterogeneity was explained by the variability both within publications (i.e., the assessment task level: 82.6%) and between publications (i.e., the individual study level: 16.7%). Our results also suggest that all six factors have significant moderator effects on scoring success magnitudes. Among these, algorithm and subject domain had significantly larger effects than the other factors, suggesting that technical features and assessment external features might be primary targets for improving MHAs and ML-based science assessments. [ABSTRACT FROM AUTHOR]

This study provides a partial-credit scoring (PCS) approach to awarding students' performance on multiple-choice items in science education. The approach is built on fundamental ideas, the critical pieces of students' understanding and knowledge to solve science problems. We link each option of the items to several specific fundamental ideas to capture their mastery patterns when an option is selected. Using these mastery patterns to order the options of items and accordingly assign credits to students, we measure students' cognitive proficiency without including additional measures (e.g. times of trial) to the test or requiring extra support (e.g. technology). Using many-facet Rasch analysis, we find that the ordered options students selected were aligned with their ability measures. The PCS yields robust psychometric quality; compared to the dichotomous scoring of multiple-choice items, it generates better item fit and separation parameters. Besides, this PCS approach helps address construct validity by modelling student responses at the option level to reflect students' mastery of fundamental ideas. [ABSTRACT FROM AUTHOR]

As cutting-edge technologies, such as machine learning (ML), are increasingly involved in science assessments, it is essential to conceptualize how assessment practices are innovated by technologies. To partially meet this need, this article focuses on ML-based science assessments and elaborates on how ML innovates assessment practices in science education. The article starts with an articulation of the "practice" nature of assessment both of learning and for learning, identifying four essential assessment practices: identifying learning goals, eliciting performance, interpreting observations, and decision-making and action-taking. I then extend a three-dimensional framework for innovative assessments, including construct, functionality, and automaticity, and based on which to conceptualize innovative assessments in three levels: substitute, transform, and redefine. Using the framework, I elaborate on how the 10 articles included in this special issue, Applying Machine Learning in Science Assessment: Opportunity and Challenge, advanced our knowledge of the innovations that ML brought to science assessment practices. I contend that the 10 articles exemplify a great deal of effort to transform the four components of assessment practices: ML allows assessments to target complex, diverse, and structural constructs, and thus better approaching the three-dimensional science learning goals of the Next Generation Science Standards (NGSS Lead States, 2013); ML extends the approaches used to eliciting performance and collecting evidence; ML provides a means to better interpreting observations and using evidence; ML supports immediate and complex decision-making and action-taking. I conclude this article by pushing the field to consider the underlying educational theories that are needed for innovative assessment practices and the necessities of establishing a "romance" between assessment practices and the relevant educational theories, which I contend are the prominent challenges to forward innovative and ML-based assessment practices in science education. [ABSTRACT FROM AUTHOR]

In response to the call for promoting three-dimensional science learning (NRC, 2012), researchers argue for developing assessment items that go beyond rote memorization tasks to ones that require deeper understanding and the use of reasoning that can improve science literacy. Such assessment items are usually performance-based constructed responses and need technology involvement to ease the burden of scoring placed on teachers. This study responds to this call by examining the use and accuracy of a machine learning text analysis protocol as an alternative to human scoring of constructed response items. The items we employed represent multiple dimensions of science learning as articulated in the 2012 NRC report. Using a sample of over 26,000 constructed responses taken by 6700 students in chemistry and physics, we trained human raters and compiled a robust training set to develop machine algorithmic models and cross-validate the machine scores. Results show that human raters yielded good (Cohen's k =.40–.75) to excellent (Cohen's k >.75) interrater reliability on the assessment items with varied numbers of dimensions. A comparison reveals that the machine scoring algorithms achieved comparable scoring accuracy to human raters on these same items. Results also show that responses with formal vocabulary (e.g., velocity) were likely to yield lower machine-human agreements, which may be associated with the fact that fewer students employed formal phrases compared with the informal alternatives. [ABSTRACT FROM AUTHOR]

This study provides a solid validity inferential network to guide the development, interpretation, and use of machine learning-based next-generation science assessments (NGSAs). Given that machine learning (ML) has been broadly implemented in the automatic scoring of constructed responses, essays, simulations, educational games, and interdisciplinary assessments to advance the evidence collection and inference of student science learning, we contend that additional validity issues arise for science assessments due to the involvement of ML. These emerging validity issues may not be addressed by prior validity frameworks developed for either non-science or non-ML assessments. We thus examine the changes brought in by ML to science assessments and identify seven critical validity issues of ML-based NGSAs: potential risk of misrepresenting the construct of interest, potential confounders due to that more variables may involve, nonalignment between interpretation and use of scores and designed learning goals, nonalignment between interpretation and use of scores and actual learning quality, nonalignment between machine scores and rubrics, limited generalizable ability of machine algorithmic models, and limited extrapolating ability of machine algorithmic models. Based on the seven validity issues identified, we propose a validity inferential network to address the cognitive, instructional, and inferential validity of ML-based NGSAs. To demonstrate the utility of this network, we present an exemplar of ML-based next-generation science assessments that was developed using a seven-step ML framework. We articulate how we used the validity inferential network to ensure accountable assessment design, as well as valid interpretation and use of machine scores. [ABSTRACT FROM AUTHOR]

This study investigated how students' and teachers' pedagogical roles in mobile learning moderated the relationship between high school students' perceived usefulness of mobile technology and the actual use frequency, as well as how students' perceived usefulness impacted their physics learning achievement. We examined 803 high school freshmen who used 20 specific functions of tablets in physics learning for five months. Based on pedagogical roles students and teachers played, 15 of the 20 functions were classified into three pedagogical categories: five for student-led, five for teacher-led, and five for collaborative functions (Journal of Science Education and Technology; Zhai, Li, & Chen, 28: 310–320, 2019). Results indicate that students perceived the collaborative functions as the most useful ones compared to student- and teacher-led functions. Also, the use frequency of mobile functions was highly aligned with students' perceived usefulness by pedagogical category, and the pedagogical category was found to moderate the association between the perceived usefulness and the actual use frequency. That is, the relationship between the perceived usefulness and the use frequency varied significantly across the three pedagogical categories. We also found the use frequency mediated the relationship between perceived usefulness and physics learning achievement particularly for the collaborative functions. The male and female students tended to have different perceptional accountability of mobile technology for their physics learning achievement. Specifically, male students' perception of collaborative functions positively predicted their physics learning achievement, while female students' perception of student-led functions positively predicted their physics learning achievement. Both male and female students' perception of teacher-led functions negatively predicted their physics learning achievement. [ABSTRACT FROM AUTHOR]

• The displacement of lung tissue can be predicted by observing the displacement of body surface. • Unsupervised learning (clustering) is capable of classifying the movement magtitudes of lung tissue and body surface, therefore contributing to better data quality. • Machine learing based regression is advantageous over classical regressions in the nonlinear lung displacement prediction, with room to improve. Lung surgery, particularly lung puncture, is challenging because respiration motion significantly displaces lung tissues (e.g., blood vessels, trachea), including targeted lesions. To improve the success of lung surgery, it is essential to determine how lung tissues move during respiration. Early work used simple mathematical models (e.g., linear models) to predict lung-tissue displacement. However, the quality and quantity of pulmonary computed tomography (CT) images used to build such models were limited, preventing their universal application. Moreover, such mathematical models are too simple to describe how lung tissues are displaced during respiration. In this work, we analyzed the relationship between body-surface and lung-tissue displacement in a large number of multi-phase pulmonary CT images to create a model predicting lung-tissue displacement. First, a systematic method based on unsupervised learning approaches was designed to calculate the displacement of the body surface and lung tissues. Second, a deep neural network model was developed to predict lung-tissue displacement, using the body-surface displacement observed in CT imaging data as input. Multi-phase pulmonary CT images from 199 patients were collected to train and test our model. The validation experiment demonstrated that the prediction accuracy based on our deep learning model reached 79.8%, which outperformed traditional machine learning approaches like the linear model (79.2%), linear support vector regression model (79.2%), and support vector regression model with radial basis function (67.2%). These results demonstrated that our method could predict lung-tissue displacement based on body-surface displacement during respiration, potentially improving the success of lung puncture procedures. [ABSTRACT FROM AUTHOR]