Lost at starting line : predicting maladaptation of university freshmen based on educational big data

The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of “at-risk” students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods. © 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology

Educational anomaly analytics : features, methods, and challenges

Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field. Copyright © 2022 Guo, Bai, Tian, Firmin and Xia

Graduate employment prediction with bias

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students’ employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record*

Quantifying Success in Science: An Overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions

Quantifying success in science : an overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011

Graduate Employment Prediction with Bias

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework

Dynamical Variations of the Global COVID-19 Pandemic Based on a SEICR Disease Model: A New Approach of Yi Hua Jie Mu

The ongoing coronavirus disease 2019 (COVID-19) pandemic has caused more than 150 million cases of infection to date and poses a serious threat to global public health. In this study, global COVID-19 data were used to examine the dynamical variations from the perspectives of immunity and contact of 84 countries across the five climate regions: tropical, arid, temperate, and cold. A new approach named Yi Hua Jie Mu is proposed to obtain the transmission rates based on the COVID-19 data between the countries with the same climate region over the Northern Hemisphere and Southern Hemisphere. Our results suggest that the COVID-19 pandemic will persist over a long period of time or enter into regular circulation in multiple periods of 1–2 years. Moreover, based on the simulated results by the COVID-19 data, it is found that the temperate and cold climate regions have higher infection rates than the tropical and arid climate regions, which indicates that climate may modulate the transmission of COVID-19. The role of the climate on the COVID-19 variations should be concluded with more data and more cautions. The non-pharmaceutical interventions still play the key role in controlling and prevention this global pandemic

Cell-Free Mitochondrial DNA in the CSF: A Potential Prognostic Biomarker of Anti-NMDAR Encephalitis

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune inflammatory brain disease that can develop a variety of neuropsychiatric presentations. However, the underlying nature of its inflammatory neuronal injury remains unclear. Mitochondrial DNA (mtDNA) is recently regarded as a damage-associated molecular pattern molecule (DAMP) that can initiate an inflammatory response. In the presenting study, we aimed to evaluate the levels of cell-free mtDNA in cerebrospinal fluid (CSF) of patients with anti-NMDAR encephalitis and to determine a potential role of cell-free mtDNA in the prognosis of anti-NMDAR encephalitis. A total of 33 patients with NMDAR encephalitis and 17 patients with other non-inflammatory disorders as controls were included in this study. The CSF levels of cell-free mtDNA were measured by quantitative polymerase chain reaction (qPCR). Cytokines including interleukin (IL)-6, IL-10, and tumor necrosis factor alpha (TNF-α) were measured by ELISA. The modified Rankin scale (mRS) score was evaluated for neurologic disabilities. Our data showed that the CSF levels of cell-free mtDNA and inflammation-associated cytokines were significantly higher in the patients with anti-NMDAR encephalitis compared with those in controls. Positive correlations were detected between the CSF levels of cell-free mtDNA and mRS scores of patients with anti-NMDAR encephalitis at both their admission and 6-month follow up. These findings suggest that the CSF level of cell-free mtDNA reflects the underlying neuroinflammatory process in patients with anti-NMDAR encephalitis and correlates with their clinical mRS scores. Therefore, cell-free mtDNA may be a potential prognostic biomarker for anti-NMDAR encephalitis

Selective deletion of endothelial cell calpain in mice reduces diabetic cardiomyopathy by improving angiogenesis

Aims/hypothesis: The role of non-cardiomyocytes in diabetic cardiomyopathy has not been fully addressed. This study investigated whether endothelial cell calpain plays a role in myocardial endothelial injury and microvascular rarefaction in diabetes, thereby contributing to diabetic cardiomyopathy. Methods: Endothelial cell-specific Capns1-knockout (KO) mice were generated. Conditions mimicking prediabetes and type 1 and type 2 diabetes were induced in these KO mice and their wild-type littermates. Myocardial function and coronary flow reserve were assessed by echocardiography. Histological analyses were performed to determine capillary density, cardiomyocyte size and fibrosis in the heart. Isolated aortas were assayed for neovascularisation. Cultured cardiac microvascular endothelial cells were stimulated with high palmitate. Angiogenesis and apoptosis were analysed. Results: Endothelial cell-specific deletion of Capns1 disrupted calpain 1 and calpain 2 in endothelial cells, reduced cardiac fibrosis and hypertrophy, and alleviated myocardial dysfunction in mouse models of diabetes without significantly affecting systemic metabolic variables. These protective effects of calpain disruption in endothelial cells were associated with an increase in myocardial capillary density (wild-type vs Capns1-KO 3646.14 ± 423.51 vs 4708.7 ± 417.93 capillary number/high-power field in prediabetes, 2999.36 ± 854.77 vs 4579.22 ± 672.56 capillary number/high-power field in type 2 diabetes and 2364.87 ± 249.57 vs 3014.63 ± 215.46 capillary number/high-power field in type 1 diabetes) and coronary flow reserve. Ex vivo analysis of neovascularisation revealed more endothelial cell sprouts from aortic rings of prediabetic and diabetic Capns1-KO mice compared with their wild-type littermates. In cultured cardiac microvascular endothelial cells, inhibition of calpain improved angiogenesis and prevented apoptosis under metabolic stress. Mechanistically, deletion of Capns1 elevated the protein levels of β-catenin in endothelial cells of Capns1-KO mice and constitutive activity of calpain 2 suppressed β-catenin protein expression in cultured endothelial cells. Upregulation of β-catenin promoted angiogenesis and inhibited apoptosis whereas knockdown of β-catenin offset the protective effects of calpain inhibition in endothelial cells under metabolic stress. Conclusions/interpretation: These results delineate a primary role of calpain in inducing cardiac endothelial cell injury and impairing neovascularisation via suppression of β-catenin, thereby promoting diabetic cardiomyopathy, and indicate that calpain is a promising therapeutic target to prevent diabetic cardiac complications