Real-time Attention Span Tracking in Online Education
Over the last decade, e-learning has revolutionized how students learn by providing them access to quality education whenever and wherever they want. However, students often get distracted because of various reasons, which affect the learning capacity to a great extent. Many researchers have been tr...
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| creator | RK, Rahul S, Shanthakumar P, Vykunth K, Sairamnath |
| description | Over the last decade, e-learning has revolutionized how students learn by
providing them access to quality education whenever and wherever they want.
However, students often get distracted because of various reasons, which affect
the learning capacity to a great extent. Many researchers have been trying to
improve the quality of online education, but we need a holistic approach to
address this issue. This paper intends to provide a mechanism that uses the
camera feed and microphone input to monitor the real-time attention level of
students during online classes. We explore various image processing techniques
and machine learning algorithms throughout this study. We propose a system that
uses five distinct non-verbal features to calculate the attention score of the
student during computer based tasks and generate real-time feedback for both
students and the organization. We can use the generated feedback as a heuristic
value to analyze the overall performance of students as well as the teaching
standards of the lecturers. |
| doi_str_mv | 10.48550/arxiv.2111.14707 |
| format | Article |
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providing them access to quality education whenever and wherever they want.
However, students often get distracted because of various reasons, which affect
the learning capacity to a great extent. Many researchers have been trying to
improve the quality of online education, but we need a holistic approach to
address this issue. This paper intends to provide a mechanism that uses the
camera feed and microphone input to monitor the real-time attention level of
students during online classes. We explore various image processing techniques
and machine learning algorithms throughout this study. We propose a system that
uses five distinct non-verbal features to calculate the attention score of the
student during computer based tasks and generate real-time feedback for both
students and the organization. We can use the generated feedback as a heuristic
value to analyze the overall performance of students as well as the teaching
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providing them access to quality education whenever and wherever they want.
However, students often get distracted because of various reasons, which affect
the learning capacity to a great extent. Many researchers have been trying to
improve the quality of online education, but we need a holistic approach to
address this issue. This paper intends to provide a mechanism that uses the
camera feed and microphone input to monitor the real-time attention level of
students during online classes. We explore various image processing techniques
and machine learning algorithms throughout this study. We propose a system that
uses five distinct non-verbal features to calculate the attention score of the
student during computer based tasks and generate real-time feedback for both
students and the organization. We can use the generated feedback as a heuristic
value to analyze the overall performance of students as well as the teaching
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providing them access to quality education whenever and wherever they want.
However, students often get distracted because of various reasons, which affect
the learning capacity to a great extent. Many researchers have been trying to
improve the quality of online education, but we need a holistic approach to
address this issue. This paper intends to provide a mechanism that uses the
camera feed and microphone input to monitor the real-time attention level of
students during online classes. We explore various image processing techniques
and machine learning algorithms throughout this study. We propose a system that
uses five distinct non-verbal features to calculate the attention score of the
student during computer based tasks and generate real-time feedback for both
students and the organization. We can use the generated feedback as a heuristic
value to analyze the overall performance of students as well as the teaching
standards of the lecturers.</abstract><doi>10.48550/arxiv.2111.14707</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Real-time Attention Span Tracking in Online Education |
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