High-quality education is one of the keys to achieving a more sustainable world. The recent COVID-19 epidemic has triggered the outbreak of online education, which has enabled both students and teachers to learn and teach at home. Meanwhile, it is now possible to record and research a large amount of learning data using online learning platforms in order to offer better intelligent educational services. Knowledge Tracing (KT), which aims to monitor students' evolving knowledge state, is a fundamental and crucial task to support these intelligent services. Therefore, an increasing amount of research attention has been paid to this emerging area and considerable progress has been made. In this survey, we propose a new taxonomy of existing basic KT models from a technical perspective and provide a comprehensive overview of these models in a systematic manner. In addition, many variants of KT models have been proposed to capture more complete learning process. We then review these variants involved in three phases of the learning process: before, during, and after the student learning, respectively. Moreover, we present several typical applications of KT in different educational scenarios. Finally, we provide some potential directions for future research in this fast-growing field.
Analysis shows that extending the age limit for grants boosts the number awarded to women, but more must be done to achieve parity, say Ying Ma and colleagues.Chemist Youyou Tu, who discovered the malaria treatment artemisinin, was the first Chinese female scientist to win a Nobel prize. CLAUDIO BRESCIANI/AFP/GETTY 3 M A Y 2 0 1 8 | V O L 5 5 7 | N A T U R E | 2 5 COMMENT © 2 0 1 8 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d . Chemist Youyou Tu worked with pharmacologist Lou Zhicen (left) on traditional Chinese medicine in the 1950s, when women's participation in the workforce in China was encouraged and protected. 3 M A Y 2 0 1 8 | V O L 5 5 7 | N A T U R E | 2 7 COMMENT XINHUA/ALAMY © 2 0 1 8 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .
Gender gaps in STEM fields have been studied for a long time, and the primary focus has been on the relationship among social support (parents and teachers), STEM beliefs (STEM interest belief, self‐efficacy belief, and value belief), and STEM career expectations. Framed in Expectancy‐Value Models, this article aimed to explore how social support affects students' STEM career expectations directly and indirectly through STEM beliefs. Further, a gender study was conducted to examine the differences in structural relations between male and female student groups using multiple‐group structural equation modeling. A total of 798 10th grade students were surveyed in mainland China. The results showed that (1) male students performed better than female students in STEM career expectations, STEM value beliefs, STEM self‐efficacy beliefs, as well as parents' and teachers' support; (2) female students' STEM career expectations could be predicted by parental support, STEM value beliefs, and STEM interest beliefs, while male students' STEM career expectations were positively influenced by parental support, STEM self‐efficacy, and STEM interest beliefs. Hence, there were apparent gender differences regarding STEM interest beliefs, STEM self‐efficacy and their relationship toward future career expectations. Specifically, STEM interest beliefs were positively correlated with STEM career expectations of female students, whereas STEM self‐efficacy could only significantly influence male students' STEM career expectations.
BackgroundThe relationship between neutrophil to lymphocyte ratio (NLR) and poor outcome of aneurysmal subarachnoid hemorrhage (aSAH) is controversial. We aim to evaluate the relationship between NLR on admission and the poor outcome after aSAH.MethodPart I: Retrospective analysis of aSAH patients in our center. Baseline characteristics of patients were collected and compared. Multivariate analysis was used to evaluate parameters independently related to poor outcome. Receiver operating characteristic (ROC) curve analysis was used to determine the best cut-off value of NLR. Part II: Systematic review and meta-analysis of relevant literature. Related literature was selected through the database. The pooled odds ratio (OR) and corresponding 95% confidence interval (CI) were calculated to evaluate the correlation between NLR and outcome measures.ResultsPart I: A total of 240 patients with aSAH were enrolled, and 52 patients had a poor outcome. Patients with poor outcome at 3 months had a higher admission NLR, Hunt & Hess score, Barrow Neurological Institute (BNI) scale score, Subarachnoid Hemorrhage Early Brain Edema Score (SEBES), and proportion of hypertension history. After adjustment, NLR at admission remained an independent predictor of poor outcome in aSAH patients (OR 0.76, 95% CI 0.69-0.83; P < 0.001). The best cut-off value of NLR in ROC analysis is 12.03 (area under the curve 0.805, 95% CI 0.735 - 0.875; P < 0.001). Part II: A total of 16 literature were included. Pooled results showed that elevated NLR was significantly associated with poor outcome (OR 1.31, 95% CI 1.14-1.49; P < 0.0001) and delayed cerebral ischemia (DCI) occurrence (OR 1.32, 95% CI 1.11-1.56; P = 0.002). The results are more reliable in large sample sizes, low NLR cut-off value, multicenter, or prospective studies.ConclusionElevated NLR is an independent predictor of poor outcome and DCI occurrence in aSAH.
Science career expectations can be affected by personal science beliefs and social supports. Framed in Expectancy-Value Models, this research studied the influence of science beliefs (science interest belief, self-efficacy belief and value belief) and social supports (parents and teachers) on students’ science career expectations by the survey of 798 10th grade students. Based on Structural Equation Model, it was found that: 1) science interest belief, self-efficacy belief, value belief and parents’ support can directly predict students' expectations of science careers; 2) the effect of student’s perception from parents and teachers support on science choice preferences and career engagement are mediated through the effects on students’ interest, self-efficacy and value in science. Therefore, teachers and parents should enhance students’ science beliefs and identity for the improvement of their science career expectations. Keywords: influencing factors, science career expectations, Structural Equation Model, 10th grade students.
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