YL
Y. Lee
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1
During a learning task, keeping a steady attentive state is detrimental for good performance. A person is subject to distraction from different sources, among which distractions originating from within him or herself or from external sources, such as ambient sound. The detection of such distraction can improve the effectiveness of a task by giving feedback when necessary. Existing researches tried to measure performance on specific activities with the use of mobile devices such as smartphones and smartwatches, and a study showed a correlation between changes of posture and distraction. This paper tackles a main question \say{How mobile devices sensors can indicate learner’s distractions in the remote learning context?}. The process to do so included the recording of raw data from the movement sensors from a smartphone and smartwatch during a reading task, which was processed to highlight movements and then used to train a Convolutional Long Short Term Memory (LSTM) model. The final produced result showed a F1 score of 0.919 on validation data and was also combined with an external model to detect distraction from ambient noise to create a multimodal model, which showed better performance than both models individually. The limitations of the data collected during the experiment and improvements for future work are also discussed.
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During a learning task, keeping a steady attentive state is detrimental for good performance. A person is subject to distraction from different sources, among which distractions originating from within him or herself or from external sources, such as ambient sound. The detection of such distraction can improve the effectiveness of a task by giving feedback when necessary. Existing researches tried to measure performance on specific activities with the use of mobile devices such as smartphones and smartwatches, and a study showed a correlation between changes of posture and distraction. This paper tackles a main question \say{How mobile devices sensors can indicate learner’s distractions in the remote learning context?}. The process to do so included the recording of raw data from the movement sensors from a smartphone and smartwatch during a reading task, which was processed to highlight movements and then used to train a Convolutional Long Short Term Memory (LSTM) model. The final produced result showed a F1 score of 0.919 on validation data and was also combined with an external model to detect distraction from ambient noise to create a multimodal model, which showed better performance than both models individually. The limitations of the data collected during the experiment and improvements for future work are also discussed.
Sustained attention is a cognitive state where the learners’ attention is completely focused on the learning environment and content-related thoughts for a continuous stretch of time. Sustained attention is vital to perform well on learning tasks, such as reading. Learning analytics platforms that detect changes in sustained attention can prevent ineffective learning by providing direct feedback to the learner. Prior research has found that eye gaze and blink patterns can be good indicators of cognitive state. In this paper we investigate the following main research question: "How can webcam-based eye gaze and blink pattern tracking indicate changes in learners' sustained attention in the remote learning context?". While other studies rely on expensive eye trackers to perform detection, this research explores the use of widely used laptop webcams for detecting changes in sustained attention. We collected webcam data through a small case study involving several different reading tasks. A machine learning classification model was trained on the collected webcam data. The resulting detection model performs well on validation data with a F1-score of 0.889. The model does not perform well on testing data however, showing that it is not usable in practice. We give several possible explanations for this behavior, most of them originating from an overfitted model due to the small size of the user study. Our findings indicate that future work should focus on different experimental settings and larger user studies.
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Sustained attention is a cognitive state where the learners’ attention is completely focused on the learning environment and content-related thoughts for a continuous stretch of time. Sustained attention is vital to perform well on learning tasks, such as reading. Learning analytics platforms that detect changes in sustained attention can prevent ineffective learning by providing direct feedback to the learner. Prior research has found that eye gaze and blink patterns can be good indicators of cognitive state. In this paper we investigate the following main research question: "How can webcam-based eye gaze and blink pattern tracking indicate changes in learners' sustained attention in the remote learning context?". While other studies rely on expensive eye trackers to perform detection, this research explores the use of widely used laptop webcams for detecting changes in sustained attention. We collected webcam data through a small case study involving several different reading tasks. A machine learning classification model was trained on the collected webcam data. The resulting detection model performs well on validation data with a F1-score of 0.889. The model does not perform well on testing data however, showing that it is not usable in practice. We give several possible explanations for this behavior, most of them originating from an overfitted model due to the small size of the user study. Our findings indicate that future work should focus on different experimental settings and larger user studies.
Remote learning, learning from home using online available materials, is becoming increasingly more common. This paper focuses on reading activities during remote learning. An important part of learning is keeping sustained attention on the learning materials, as a shift from sustained attention to internal thought or mind-wandering oftentimes decreases the learning performance. Yet this shift is very common during daily activities. It is therefore important for a remote E-learning system to be aware of these attention shifts and allow it to intervene when necessary. Prior research indicates that facial emotion detection and participant's body temperature are generally missing as data modalities. Especially facial emotion recognition may contribute to this area of research, as facial emotions may indicate a learner's affective state during the learning process, and detection of this can be done using a simple consumer laptop web camera. Therefore this paper aims to investigate the feasibility of using a standard laptop web camera to detect facial emotions and a cheap Infrared Temperature Sensor (IR-sensor) to detect the loss of sustained attention. Two experiments were conducted to gather attentive and inattentive data. From this data, features were extracted which were used in multiple machine learning models. The created Machine Learning (ML) models worked well on synthetic test and validation data, but they performed poorly in practice. Our main hypothesis for this is overfitting of the ML models on the data, as due to the Coronavirus Disease (COVID-19) no more than three participants partook in the user studies. Overall, the conclusion is that both emotional facial detection and participant body temperature show great potential to detect sustained attention, but further research needs to be done with a larger group of participants to confirm this.
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Remote learning, learning from home using online available materials, is becoming increasingly more common. This paper focuses on reading activities during remote learning. An important part of learning is keeping sustained attention on the learning materials, as a shift from sustained attention to internal thought or mind-wandering oftentimes decreases the learning performance. Yet this shift is very common during daily activities. It is therefore important for a remote E-learning system to be aware of these attention shifts and allow it to intervene when necessary. Prior research indicates that facial emotion detection and participant's body temperature are generally missing as data modalities. Especially facial emotion recognition may contribute to this area of research, as facial emotions may indicate a learner's affective state during the learning process, and detection of this can be done using a simple consumer laptop web camera. Therefore this paper aims to investigate the feasibility of using a standard laptop web camera to detect facial emotions and a cheap Infrared Temperature Sensor (IR-sensor) to detect the loss of sustained attention. Two experiments were conducted to gather attentive and inattentive data. From this data, features were extracted which were used in multiple machine learning models. The created Machine Learning (ML) models worked well on synthetic test and validation data, but they performed poorly in practice. Our main hypothesis for this is overfitting of the ML models on the data, as due to the Coronavirus Disease (COVID-19) no more than three participants partook in the user studies. Overall, the conclusion is that both emotional facial detection and participant body temperature show great potential to detect sustained attention, but further research needs to be done with a larger group of participants to confirm this.
In this research, a learner’s sustained attention in the remote learning context will be studied by collecting data from different sensors. By combining the results of these sensors in a multi-modal analytics tool, the estimation of the learner’s sustained attention can hopefully be improved. This research will mainly focus on microphone recordings of ambient sound in a learners room. The main research question of this research was "How can ambient noise sensing aid in a multi-modal analytics tool to track sustained attention?". The multi-modal learning analytics tool, if accurate enough, could potentially be used by teachers to make their material more engaging and could help learner’s to keep their focus while performing a learning task (Schneider et al., 2015). The research resulted in a model with 61% accuracy. This percentage needs to be further researched, since because of the COVID situation, not enough data could be collected to train the model. Because of the relatively low accuracy of the model, it was found that ambient noise sensing can aid the multi-modal analytics tool to some extent by adding some data-points it is certain about, when the mobile movement tracking model does not detect a distraction. If the model improves in future research, the model could be able to help mobile movement tracking model, even if the mobile movement tracking model already predicts a distraction with bigger then 50% certainty.
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In this research, a learner’s sustained attention in the remote learning context will be studied by collecting data from different sensors. By combining the results of these sensors in a multi-modal analytics tool, the estimation of the learner’s sustained attention can hopefully be improved. This research will mainly focus on microphone recordings of ambient sound in a learners room. The main research question of this research was "How can ambient noise sensing aid in a multi-modal analytics tool to track sustained attention?". The multi-modal learning analytics tool, if accurate enough, could potentially be used by teachers to make their material more engaging and could help learner’s to keep their focus while performing a learning task (Schneider et al., 2015). The research resulted in a model with 61% accuracy. This percentage needs to be further researched, since because of the COVID situation, not enough data could be collected to train the model. Because of the relatively low accuracy of the model, it was found that ambient noise sensing can aid the multi-modal analytics tool to some extent by adding some data-points it is certain about, when the mobile movement tracking model does not detect a distraction. If the model improves in future research, the model could be able to help mobile movement tracking model, even if the mobile movement tracking model already predicts a distraction with bigger then 50% certainty.