HHA: An Attentive Prediction Model for Academic Abnormality

Zeng, Yawen; OuYang, Yong; Gao, Rong; Qiu, Ye; Yu, Yonghong; Wang, Chunzhi

doi:10.1109/access.2020.3007750

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…The essence of Attention is to assign weights to different elements, highlight essential features, and improve network performance. For example, Zeng et al [37] utilized High-Level Attention to measure the importance of different courses and optimize academic abnormal prediction tasks. In addition, Ouyang et al [8] adopted a multi-layer Attention network to balance the weights of different student academic achievement to improve the accuracy of graduation development prediction.…”

Section: Transformermentioning

confidence: 99%

“…The data are all statistics and input into the school system according to the real situation of students. This paper uses the augmented version of student dataset released in the paper [8] and [37] to test the model. The dataset contains all the information about the students, including dormitory classes, required courses and elective courses, campus card usage records, etc.…”

Section: Datasetmentioning

confidence: 99%

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Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

Self Cite

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As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students' learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students' graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student's performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students' features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

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Section: Transformermentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Social-path embedding-based transformer for graduation development prediction

Yang

Ouyang

et al. 2022

Appl Intell

Self Cite

View full text Add to dashboard Cite

show abstract

“…You et al [41] proposed a hybrid neural network method based on a high-order attention mechanism, using generative adversarial networks to simulate students' learning behavior, mine missing data, and quickly classify students' studies, but the model considering online learning detection is not very well. Zeng et al [42] proposed an attentive prediction model for academic abnormalities. Nie et al [43] aimed at the problem of precise poverty alleviation in colleges and universities, based on student behavior data, combined with the time series characteristics of college data, extracted the multidimensional characteristics of students' basic information and behavior data and proposed a CW-LSTM algorithm based on deep learning theory for prediction.…”

Section: Research On Abnormal Behaviors Of Students Deng Et Almentioning

confidence: 99%

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Wang

Wen

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.

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“…But the model failed to perform the complexity analysis. Hybrid Neural Network Model based on High-Order Attention Mechanism, (HHA) was developed in [19] for academic abnormality prediction. However, it failed to select the expressive features for reducing the dimensionality of the dataset.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Tukey Regressive Hoover Indexed Deep Shift-Invariant Neural Network for Student Behavior Prediction

Pushpavalli¹,

Mary²

2022

IJRITCC

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Prediction of student performance in the academic field creates significant challenges in developing reliable and accurate diagnosis models. Through the use of online learning behavior data, this paper may assist teachers in identifying students with learning challenges in advance and providing timely assistance. A novel technique called Tukey Regressive Hoover indexed Deep Shift Invariant Structure Neural Network (TRHIDSISNN) Model is introduced for student behaviour analysis with lesser time consumption. Initially, the student data and features are collected and transmitted to the input layer. After that, the features of collected student data are analyzed in hidden layer 1 with help of the Tukey Regression. The correlation between one or more independent features is identified to find the dependent feature. The relevant features are sent to the hidden layer 2. In that layer, the Hoover index is applied for analyzing the training and testing features. Finally, the hidden layer result is sent to the output layer where the hyperbolic tangent activation function is used to classify the data that belongs to that particular class. Based on the classification, the student grade level is predicted as high, medium and low based on their behavior gets displayed. Experimental assessment is carried out using different parameters such as prediction accuracy, false-positive rate, prediction time, and space complexity with respect to the number of student data. The discussed results show that when compared to state-of-the-art approaches, the suggested TRHIDSISNN model achieves higher accuracy with shorter prediction times.

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HHA: An Attentive Prediction Model for Academic Abnormality

Cited by 8 publications

References 32 publications

Social-path embedding-based transformer for graduation development prediction

Social-path embedding-based transformer for graduation development prediction

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

Tukey Regressive Hoover Indexed Deep Shift-Invariant Neural Network for Student Behavior Prediction

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