Educational big data : predictions, applications and challenges
- Authors: Bai, Xiaomei , Zhang, Fuli , Li, Jinzhou , Guo, Teng , Xia, Feng
- Date: 2021
- Type: Text , Journal article , Review
- Relation: Big Data Research Vol. 26, no. (2021), p.
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- Description: Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data. © 2021 Elsevier Inc. **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**
Tracing the Pace of COVID-19 research : topic modeling and evolution
- Authors: Liu, Jiaying , Nie, Hansong , Li, Shihao , Ren, Jing , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Big Data Research Vol. 25, no. (2021), p.
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- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren and Feng Xia" is provided in this record**
- Description: COVID-19 has been spreading rapidly around the world. With the growing attention on the deadly pandemic, discussions and research on COVID-19 are rapidly increasing to exchange latest findings with the hope to accelerate the pace of finding a cure. As a branch of information technology, artificial intelligence (AI) has greatly expedited the development of human society. In this paper, we investigate and visualize the on-going advancements of early scientific research on COVID-19 from the perspective of AI. By adopting the Latent Dirichlet Allocation (LDA) model, this paper allocates the research articles into 50 key research topics pertinent to COVID-19 according to their abstracts. We present an overview of early studies of the COVID-19 crisis at different scales including referencing/citation behavior, topic variation and their inner interactions. We also identify innovative papers that are regarded as the cornerstones in the development of COVID-19 research. The results unveil the focus of scientific research, thereby giving deep insights into how the academic society contributes to combating the COVID-19 pandemic. © 2021 Elsevier Inc.
Data-driven computational social science : A survey
- Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
- Date: 2020
- Type: Text , Journal article
- Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
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- Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.
A logical approach to experience-based reasoning
- Authors: Sun, Zhaohao
- Date: 2017
- Type: Text , Journal article , Review
- Relation: New Mathematics and Natural Computation Vol. 13, no. 1 (2017), p. 21-40
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- Description: Experience-based reasoning (EBR) is a paradigm used in almost every human activity as a part of human reasoning. However, EBR has not been seriously studied from a logical viewpoint. This paper will attempt to fill this gap by providing a unified logical approach to EBR. More specifically, this paper first examines EBR and inference rules. Then it proposes eight different rules of inference for EBR, which cover all possible EBRs from a logical viewpoint. These eight different rules of inference constitute the fundamentals for all EBR paradigms, and therefore will be the theoretical foundation for EBR. The proposed approach will facilitate research and development of EBR, human reasoning, and common sense reasoning. © 2017 World Scientific Publishing Company.
A mathematical foundation of big data
- Authors: Sun, Zhaohao , Wang, Paul
- Date: 2017
- Type: Text , Journal article
- Relation: New Mathematics and Natural Computation Vol. 13, no. 2 (2017), p. 83-99
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- Description: The recent research evolution on big data has brought exciting aspiration to mathematicians, computer scientists and business professionals alike. However, the lack of a sound mathematical foundation presents itself as a real challenge amidst the swarm of big data marketing activities. This paper intends to propose a possible mathematical theory as a foundation for big data research. Specifically, we propose the concept of the adjective "big" as a mathematical operator, furthermore, the concept of so-called "big" logically and naturally fits the concept of being "linguistics variable" as per fuzzy logic research community for decades. The consequence of adopting such a mathematical modeling can be profoundly considered as an abstraction of the technologies, systems, tools for data management and processing that transforms data into big data. In addition, the concept of infinity of the big data is based on the theory of calculus and the set theory. Furthermore, the concept of relativity of the big data, as we find out, is based on the operations of the fuzzy subsets theory. The proposed approach in this paper, we hope, can facilitate and open up more opportunities for big data research and developments on big data analytics, business analytics, big data intelligence, big data computing as well as big data science. © 2017 World Scientific Publishing Company.
A computing perspective on scientific chinese trinity
- Authors: Sun, Zhaohao , Wang, Paul
- Date: 2013
- Type: Text , Journal article
- Relation: New Mathematics and Natural Computation Vol. 9, no. 2 (2013), p. 129-152
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- Description: The unprecedented and rapid development of the Chinese economy has been vividly displayed in front of the whole world to see. The attention has been particularly acute for the academic community and career politician alike. Ironically, this rapid economic miracle of China has been built on an unsound and often even questionable foundation of Chinese words, language and culture, of which we call them "Chinese trinity". This paper deals with the Chinese trinity from a computing science perspective. This paper argues the reform in scientific Chinese trinity with an emphasis of the word "scientific" ought to play a key role for further Chinese economic development and to launch a much improved contemporary Chinese society on a solid foundation. In addition, this paper proposes specifically ten computing paradigms and examines critically their potential impacts on scientific Chinese trinity. Finally, we feel the very focused approaches as proposed here might inspire as well as provide a much needed road map toward the goal of the scientific Chinese trinity. Judiciously chosen vigorous research projects appear to be indispensable. The unfortunate well known and long overdue reform has finally been rescued by the pressure of the information revolution coming of age. © 2013 World Scientific Publishing Company.
- Description: 2003011223
A basic theory of intelligent finance
- Authors: Pan, Heping
- Date: 2011
- Type: Text , Journal article
- Relation: New Mathematics and Natural Computation Vol. 7, no. 2 (May 2011), p. 197-227
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- Description: This paper presents a basic theory of intelligent finance as a new paradigm of financial investment. It is assumed that the financial market is always in a state of swing between efficient and inefficient modes on multiple levels of time scale; it is possible to go beyond the efficient market theory to study the dynamic evolving process of the market between equilibrium and far-from-equilibrium; there are robust dynamic patterns in this evolving process, which may be exploitable via intelligent trading systems. On the foundation of the four principles - comprehensive, predictive, dynamic and strategic, the basic theory takes the information sources into the loop as the starting points for all the market analysis, introducing the scale space of time into the pricing process analysis in order to detect and capture trends, cycles and seasonality on multiple intrinsic levels of time scale which are then used as the dynamic basis for constructing and managing portfolios. In stock markets, the theory exhibits itself in the form of an Intelligent Dynamic Portfolio Theory, which integrates predictive modeling of a bullbear market cycle, sector rotation, and portfolio optimization with a reactive trend following trading strategy.
Predictability of moving average rules and nonlinear properties of stock returns: Evidence from the China stock market.
- Authors: Wang, Zhigang , Zeng, Yong , Pan, Heping , Li, Ping
- Date: 2011
- Type: Text , Journal article
- Relation: New mathematics and natural computation Vol. 7, no. 3 (May 2011 2011), p. 267-279
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- Description: This paper investigates t he predictability of moving average rules for the China stock market. We find that buy signals generate higher returns and less volatility, while returns following sell signals are negative and more volatile. Moreover, the bootstrapping results indicate that the asymmetrical patterns of return and volatility between buy and sell signals cannot be explained by four popular linear models of returns, especially the phenomenon of negative sell returns. We then test the nonlinear dynamic process of returns. Although the existing artificial neural network (ANN) model can replicate the negative sell returns, it fails to capture the volatility patterns of buy and sell returns. Furthermore, we introduce the conditional heteroskedasticity structure into the ANN model and find that the revised ANN model cannot only explain the predictability of returns, but can also capture the patterns of buy and sell volatility, which are never achieved by any linear model of returns tested in the related literature. Therefore, we conclude that the moving average trading rules can pick up some of the hidden nonlinear patterns in the dynamic process of stock returns, which may be the reason why they can be used to predict price changes.
Preface
- Authors: Pan, Heping , Hayward, Serge
- Date: 2011
- Type: Text , Journal article
- Relation: New Mathematics and Natural Computation Vol. 7, no. 2 (2011), p. 187-196
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