Student Class Behavior Dataset: a video dataset for recognizing, detecting, and captioning students’ behaviors in classroom scenes

Sun, Bo; Wu, Yong; Zhao, Kaijie; He, Jun; Yu, Lejun; Yan, Huanqing; Luo, Ao

doi:10.1007/s00521-020-05587-y

Cited by 28 publications

(11 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sun et. al [ 42 ] have also worked on classroom videos and have contributed a dataset but their focus is on students’ classroom behavior, not teachers, e.g. a student is listening, yawning, sleeping, etc.…”

Section: Related Workmentioning

confidence: 99%

Employing automatic content recognition for teaching methodology analysis in classroom videos

et al. 2022

View full text Add to dashboard Cite

A teacher plays a pivotal role in grooming a society and paves way for its social and economic developments. Teaching is a dynamic role and demands continuous adaptation. A teacher adopts teaching techniques suitable for a certain discipline and a situation. A thorough, detailed, and impartial observation of a teacher is a desideratum for adaptation of an effective teaching methodology and it is a laborious exercise. An automatic strategy for analyzing a teacher’s teaching methodology in a classroom environment is suggested in this work. The proposed strategy recognizes a teacher’s actions in videos while he is delivering lectures. In this study, 3D CNN and Conv2DLSTM with time-distributed layers are used for experimentation. A range of actions are recognized for a complete classroom session during experimentation and the reported results are considered effective for analysis of a teacher’s teaching technique.

show abstract

Section: Related Workmentioning

confidence: 99%

Employing automatic content recognition for teaching methodology analysis in classroom videos

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Our dataset comes from the opensource code dataset of Sun et al [34]. It covers the student behaviors identified, detected, and labeled in class.…”

Section: Datasetmentioning

confidence: 99%

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

Xie¹,

Zhang²,

Liu³

2021

View full text Add to dashboard Cite

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.

show abstract

“…Currently, Kinetics has become the baseline dataset for benchmarking human action models. A similar dataset from EduNet is the student class behavior dataset [ 15 ]. The student class behavior dataset analyzes student behavior during master’s courses.…”

Section: Related Workmentioning

confidence: 99%

“…From Table 4 , it is observed that, to date, only two datasets involving classroom actions, refs. [ 15 , 36 ], are available. The dataset proposed by [ 36 ] has fewer video clips with only 817 clips.…”

Section: Dataset Detailsmentioning

confidence: 99%

“…In the era of AI, research into the education system can boost the quality of education by utilizing action recognition from video data. For example, a student dataset was prepared [ 15 ] to recognize, detect, and caption student behaviors in a classroom; it contained 11 action categories. Another work (for student action recognition) was conducted by the authors of [ 16 ], which included seven action categories.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EduNet: A New Video Dataset for Understanding Human Activity in the Classroom Environment

Sharma

Gupta

Kumar

et al. 2021

Sensors

View full text Add to dashboard Cite

Human action recognition in videos has become a popular research area in artificial intelligence (AI) technology. In the past few years, this research has accelerated in areas such as sports, daily activities, kitchen activities, etc., due to developments in the benchmarks proposed for human action recognition datasets in these areas. However, there is little research in the benchmarking datasets for human activity recognition in educational environments. Therefore, we developed a dataset of teacher and student activities to expand the research in the education domain. This paper proposes a new dataset, called EduNet, for a novel approach towards developing human action recognition datasets in classroom environments. EduNet has 20 action classes, containing around 7851 manually annotated clips extracted from YouTube videos, and recorded in an actual classroom environment. Each action category has a minimum of 200 clips, and the total duration is approximately 12 h. To the best of our knowledge, EduNet is the first dataset specially prepared for classroom monitoring for both teacher and student activities. It is also a challenging dataset of actions as it has many clips (and due to the unconstrained nature of the clips). We compared the performance of the EduNet dataset with benchmark video datasets UCF101 and HMDB51 on a standard I3D-ResNet-50 model, which resulted in 72.3% accuracy. The development of a new benchmark dataset for the education domain will benefit future research concerning classroom monitoring systems. The EduNet dataset is a collection of classroom activities from 1 to 12 standard schools.

show abstract

Student Class Behavior Dataset: a video dataset for recognizing, detecting, and captioning students’ behaviors in classroom scenes

Cited by 28 publications

References 57 publications

Employing automatic content recognition for teaching methodology analysis in classroom videos

Employing automatic content recognition for teaching methodology analysis in classroom videos

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

EduNet: A New Video Dataset for Understanding Human Activity in the Classroom Environment

Contact Info

Product

Resources

About