eduCircle: Visualizing Spatial Temporal Features of Student Performance from Campus Activity and Consumption Data

Wu, Yifan; Gong, Rui; Cao, Yi; Wen, Changjiang; Teng, Zhiyao; Pu, Juan

doi:10.1007/978-3-319-46771-9_41

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…(2) Model training In each iteration, 8 samples were randomly selected, and 16 continuous frames were extracted by random from each sample. Then, the spatiotemporal data acquired by KMC from the students were fused by the scheme of Lu [6], Wu et al [13]. To evaluate the recognition effect, each model was trained and evaluated by the training and test sets.…”

Section: Training Results Analysismentioning

confidence: 99%

“…For example, Buniyamin et al [12] classified and predicted student scores based on the data of campus information system. Wu et al [13] intuitively demonstrated the spatiotemporal features of student scores with the data on campus activities and consumption. Zhou and Xiao [14] predicted the information that interests college students according to the inspection data on students.…”

Section: Literature Reviewmentioning

confidence: 99%

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Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Xie¹,

Zhang²,

Liu³

2021

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Artificial intelligence and fifth generation (5G) technology are widely adopted to evaluate the classroom poses of college students, with the help of campus video surveillance equipment. To ensure the effective learning in class, it is important to detect and intervene in abnormal behaviors like sleeping and using cellphones in time. Based on spatiotemporal representation learning, this paper presents a deep learning algorithm to evaluate classroom poses of college students. Firstly, feature engineering was adopted to mine the moving trajectories of college students, which were used to determine student distribution and establish a classroom prewarning system. Then, k-means clustering (KMC) was employed for cluster analysis on different student groups, and identify the features of each group. For a specific student group, the classroom surveillance video was decomposed into several frames; the edge of each frame was extracted by edge detection algorithm, and imported to the proposed convolutional neural network (CNN). Experimental results show that our algorithm is 5% more accurate than the benchmark three-dimensional CNN (C3D), making it an effective tool to recognize abnormal behaviors of college students in class.

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Section: Training Results Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Xie¹,

Zhang²,

Liu³

2021

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“…These data describe students' daily behavior on campus from many aspects, which makes the multi-source behavior data available for in-depth analysis of their associations. Previous works based on behavior data has addressed topics such as constructing student social networks [1,2], predicting academic performance [3][4][5][6][7], and forecasting career choices [8]. These works showed that it is possible to analyze students' lives through their behavior data.…”

Section: Introductionmentioning

confidence: 99%

Visual Analytic Method for Students’ Association via Modularity Optimization

Xiao-yong

Zhang

et al. 2020

Applied Sciences

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Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the “smart campus,” many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students’ associations. In this paper, we propose a visual analytic method to construct students’ association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis.

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You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement

et al. 2020

J. Comput. Sci. Technol.

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eduCircle: Visualizing Spatial Temporal Features of Student Performance from Campus Activity and Consumption Data

Cited by 3 publications

References 6 publications

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

Visual Analytic Method for Students’ Association via Modularity Optimization

You Are How You Behave – Spatiotemporal Representation Learning for College Student Academic Achievement

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