<p>In recent years, with the popularization of online education, real-time monitoring of learning engagement has become a key challenge for scholars. Existing studies mainly rely on questionnaires and physiological signal detection, which have limitations such as high subjectivity, poor real-time performance, and expensive equipment. Previous research has shown that head pose is closely related to cognitive state. However, current estimation models require substantial computational resources, making real-time deployment on mobile devices challenging. In this study, we validate the significant correlation between head pose and learning engagement based on the DAiSEE dataset (8,925 video clips) and propose a lightweight head pose estimation method. The LightNet proposed in this paper uses an improved feature extraction module (MG-Net) and an Attention-based multi-scale fusion model (AMF). Experiments conducted on the 300W-LP and BIWI benchmark datasets demonstrate that, compared with existing state-of-the-art methods, LightNet substantially reduces model complexity by decreasing the number of parameters to just 0.45<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44443_2025_187_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="38" /> </InlineMediaObject> <EquationSource Format="TEX">$\times 10^6$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>×</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </math></EquationSource> </InlineEquation>, representing over 90% reduction in model size. Despite this significant compression, LightNet maintains a high level of accuracy, with the mean absolute error (MAE) increasing by only 0.15°, indicating a minimal loss in prediction precision. Moreover, the model achieves a notable improvement in processing speed, exceeding 50% increase relative to baseline approaches. This combination of a lightweight architecture, competitive accuracy, and accelerated inference speed underscores LightNet’s effectiveness and its potential suitability for real-time applications. This study not only expands the application of head pose in education but also provides a feasible solution for real-time engagement monitoring on resource-constrained devices.</p>

LightNet: a lightweight head pose estimation model for online education and its application to engagement assessment

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2025年8月18日

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Lin Zheng,
Jinlong Li,
Zhanbo Zhu,
Weidong Ji

摘要

In recent years, with the popularization of online education, real-time monitoring of learning engagement has become a key challenge for scholars. Existing studies mainly rely on questionnaires and physiological signal detection, which have limitations such as high subjectivity, poor real-time performance, and expensive equipment. Previous research has shown that head pose is closely related to cognitive state. However, current estimation models require substantial computational resources, making real-time deployment on mobile devices challenging. In this study, we validate the significant correlation between head pose and learning engagement based on the DAiSEE dataset (8,925 video clips) and propose a lightweight head pose estimation method. The LightNet proposed in this paper uses an improved feature extraction module (MG-Net) and an Attention-based multi-scale fusion model (AMF). Experiments conducted on the 300W-LP and BIWI benchmark datasets demonstrate that, compared with existing state-of-the-art methods, LightNet substantially reduces model complexity by decreasing the number of parameters to just 0.45 $\times 10^6$ $\times 10^{6}$ , representing over 90% reduction in model size. Despite this significant compression, LightNet maintains a high level of accuracy, with the mean absolute error (MAE) increasing by only 0.15°, indicating a minimal loss in prediction precision. Moreover, the model achieves a notable improvement in processing speed, exceeding 50% increase relative to baseline approaches. This combination of a lightweight architecture, competitive accuracy, and accelerated inference speed underscores LightNet’s effectiveness and its potential suitability for real-time applications. This study not only expands the application of head pose in education but also provides a feasible solution for real-time engagement monitoring on resource-constrained devices.