# An Optimized CNN Model for Engagement Recognition in an E-Learning Environment
## 7 Conclusions and Future Work  
This paper proposes an optimized CNN model based on the lightweight architecture, ShuffleNet v2. We chose the ShuffleNet v2 0.5x structure to minimize computational complexity. We then introduced an attention mechanism for the model using SE blocks and improved the activation function. To evaluate and compare the performance, we selected three other CNN models: Inception v3, ResNet and ShuffleNet v2. Together with our proposed model, the selected models were then trained for engagement recognition. We used accuracy, F1-score, FLOPs and speed to measure the performance of these models. Our optimized ShuffleNet v2 model had the best performance based on the results of our experiment. Moreover, our model also achieved the highest accuracy when compared with the other published models or methods using the same dataset. As a lightweight framework, the optimized ShuffleNet v2 model is suitable for deployment on mobile platforms in E-learning environments.  
In this paper, although our optimized ShuffleNet model with an attention mechanism reaches acceptable accuracy when compared with engagement-recognition models designed and implemented in other studies, we prioritize practicality. We had good performance concerning model complexity and recognition speed, but there is still room for improvement in terms of accuracy. For one, we found that the scale of the video dataset we used was still not large enough. Further, our model does not analyze timing-related information. Concerning the first issue, there are very few research studies and datasets in the area of engagement recognition. Even the DAiSEE dataset suffers from unbalanced sample distribution. There are far fewer samples with a low engagement level than with a high engagement level. This situation cannot be greatly improved with the current dataset. However, testing our proposed model with a larger and more-balanced sample could be a focus of future research work. Concerning the second issue, it must be noted that when optimizing ShuffleNet, we considered that increasing the analysis of timing information would inevitably affect the amount of calculation required for the model, which would undercut our goal of being lightweight. The expansion of the size of the dataset and the use of lightweight models that can analyze time-series information are other potential areas for future studies. Current engagement-recognition models are limited to using only CNNs. Future researchers can compare the performance other neural network structures or lightweight models. In future experiments in this area, researchers can also consider optimizing other variables, such as using a larger batch size, increasing the number of layers in the neural network or using a higher sampling rate for image augmentation for possibly better results. Applying our proposed model to detect engagement in real-life higher education E-learning environments is another main direction for future research work. Given that ShuffleNet v2 is a lightweight neural network, follow-up studies based on micro devices, such as tablets and mobile phones containing GPU chips, can be considered to explore the feasibility of building miniaturized engagement-detection devices.  
Conceptualization, Y.H., Z.J. and K.Z.; methodology, Y.H., Z.J. and K.Z.; software, Z.J. and K.Z.; validation, Z.J. and K.Z.; formal analysis, Z.J. and K.Z.; investigation, Y.H.; resources, Y.H., Z.J. and K.Z.; data curation, Y.H., Z.J. and K.Z.; writing--original draft preparation, Y.H., Z.J. and K.Z.; writing--review and editing, Y.H.; visualization, Z.J. and K.Z.; supervision, Y.H.; project administration, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.