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DEFINE: friendship detection based on node enhancement

- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng

  • Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
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  • Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
  • Description: E1

Quantifying success in science : an overview

- Bai, Xiaomei, Pan, Habxiao, Hou, Jie, Guo, Teng, Lee, Ivan, Xia, Feng

  • Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
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  • Description: 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.
  • Description: 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

- Guo, Teng, Xia, Feng, Zhen, Shihao, Bai, Xiaomei, Zhang, Dongyu

  • Authors: Guo, Teng , Xia, Feng , Zhen, Shihao , Bai, Xiaomei , Zhang, Dongyu
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence p. 670-677
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  • Description: 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**

In your face : sentiment analysis of metaphor with facial expressive features

- Zhang, Dongyu, Zhang, Minghao, Guo, Teng, Peng, Ciyuan, Saikrishna, Vidya, Xia, Feng

  • Authors: Zhang, Dongyu , Zhang, Minghao , Guo, Teng , Peng, Ciyuan , Saikrishna, Vidya , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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  • Description: Metaphor plays an important role in human communication, which often conveys and evokes sentiments. Numerous approaches to sentiment analysis of metaphors have thus gained attention in natural language processing (NLP). The primary focus of these approaches is on linguistic features and text rather than other modal information and data. However, visual features such as facial expressions also play an important role in expressing sentiments. In this paper, we present a novel neural network approach to sentiment analysis of metaphorical expressions that combines both linguistic and visual features and refer to it as the multimodal model approach. For this, we create a Chinese dataset, containing textual data from metaphorical sentences along with visual data on synchronized facial images. The experimental results indicate that our multimodal model outperforms several other linguistic and visual models, and also outperforms the state-of-the-art methods. The contribution is realized in terms of novelty of the approach and creation of a new, sizeable, and scarce dataset with linguistic and synchronized facial expressive image data. The dataset is particularly useful in languages other than English and the approach addresses one of the most challenging NLP issue: sentiment analysis in metaphor. © 2021 IEEE.

ANSWER : generating information dissemination network on campus

- Qing, Qing, Guo, Teng, Zhang, Dongyu, Xia, Feng

  • Authors: Qing, Qing , Guo, Teng , Zhang, Dongyu , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
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  • Description: Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.

Web of students : class-level friendship network discovery from educational big data

- Guo, Teng, Tang, Tang, Zhang, Dongyu, Li, Jianxin, Xia, Feng

  • Authors: Guo, Teng , Tang, Tang , Zhang, Dongyu , Li, Jianxin , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Web Information Systems Engineering, WISE 2021 p. 497-511
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  • Description: Classmate friendships are a major aspect of university social experience. Taking classes together is one of the main ways for students to build friendships. Consequently, class-level friendship networks have attracted tremendous attention from researchers. They are also very useful in student support and early intervention. However, these networks are normally invisible for educators. Discovering such an important web of students effectively is a pressing problem. Against this background, we propose a data-driven framework called CANDY which automatically discovers the class-level friendship networks based on educational big data. We first represent features through representation learning methods. Secondly, the data is augmented with the randomly shuffling method. Thirdly, a conditional generative adversarial network model is used to mine the class-level friendship networks. A deep adversarial optimization strategy is proposed here for problems caused by network sparsity. To evaluate the performance of the proposed approach, we build a real-world dataset that contains rich student information. Extensive experiments have been conducted and the results demonstrate the effectiveness of our framework. © 2021, Springer Nature Switzerland AG.

Educational big data : predictions, applications and challenges

- Bai, Xiaomei, Zhang, Fuli, Li, Jinzhou, Guo, Teng, Xia, Feng

  • 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**

Educational anomaly analytics : features, methods, and challenges

- Guo, Teng, Bai, Xiaomei, Tian, Xue, Firmin, Selena, Xia, Feng

  • Authors: Guo, Teng , Bai, Xiaomei , Tian, Xue , Firmin, Selena , Xia, Feng
  • Date: 2022
  • Type: Text , Journal article , Review
  • Relation: Frontiers in Big Data Vol. 4, no. (2022), p.
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  • Description: 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.

Predicting mental health problems with personality, behavior, and social networks

- Zhang, Dongyu, Guo, Teng, Han, Shiyu, Vahabli, Sadaf, Naseriparsa, Mehdi, Xia, Feng

  • Authors: Zhang, Dongyu , Guo, Teng , Han, Shiyu , Vahabli, Sadaf , Naseriparsa, Mehdi , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE International Conference on Big Data, Big Data 2021, virtual online, 15-18 December 2021, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 p. 4537-4546
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  • Description: Mental health is an integral part of human health and well-being. Unhealthy mentality leads to serious consequences such as self-mutilation and suicide, especially for college students. While the literature focused on analysing the relationship between mental health and a single factor such as personality or behavior, accurate prediction is yet to be achieved due to the lack of cross-dimensional analysis and multi-dimensional joint prediction. To this end, this work proposes leveraging multiple factors from three crucial dimensions of mental health: behaviors, personality, and social networks. We recruited 490 college students, and collected their behavioral records from smart cards. In addition, we extracted their psychological traits from questionnaires, and social networks by conducting the survey on the nominating community members. We created a neural network-based model to integrate behavioral, psychological, and social network factors to predict mental health problems. The experimental results verify the efficacy of the proposed model, and demonstrate that the classification model of various factors effectively predicts the students' mental issues. © 2021 IEEE.

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

- Guo, Teng, Bai, Xiaomei, Zhen, Shihao, Abid, Shagufta, Xia, Feng

  • Authors: Guo, Teng , Bai, Xiaomei , Zhen, Shihao , Abid, Shagufta , Xia, Feng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of the Association for Information Science and Technology Vol. 74, no. 1 (2023), p. 17-32
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  • Description: 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.

Multimodal educational data fusion for students' mental health detection

- Guo, Teng, Zhao, Wenhong, Alrashoud, Mubarak, Tolba, Amr, Firmin, Selena, Xia, Feng

  • Authors: Guo, Teng , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Firmin, Selena , Xia, Feng
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 10, no. (2022), p. 70370-70382
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  • Description: Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse data on social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to represent students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines. © 2013 IEEE.

KIDNet : a knowledge-aware neural network model for academic performance prediction

- Tang, Tao, Hou, Jie, Guo, Teng, Bai, Xiaomei, Tian, Xue, Noori Hoshyar, Azadeh

  • Authors: Tang, Tao , Hou, Jie , Guo, Teng , Bai, Xiaomei , Tian, Xue , Noori Hoshyar, Azadeh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021, Virtual, Online14-17 December 2021, ACM International Conference Proceeding Series p. 37-44
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  • Description: Academic performance prediction and analysis in educational data mining is meaningful for instructors to know the student's ongoing learning status, and also provide appropriate help to students as early as possible if academic difficulties appear. In this paper, we first collect the basic information of students and courses as features. Then, we propose a novel knowledge extraction framework to obtain course knowledge features to reinforce feature groups. The comparative analyses of the knowledge similarity and average grades of the courses in all terms demonstrate a strong correlation between them. Furthermore, we build the Knowledge Interaction Discovery Network (KIDNet) model, based on factorization machine (FM) and deep neural network (DNN) algorithms. This model uses FM to model lower-order interactions of sparse features and employs DNN to model higher-order interactions of both dense and sparse features. The effectiveness of KIDNet has been validated by conducting experiments based on a real-world dataset. © 2021 ACM.
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