This work was developed with funding from Programa Prometeo, project ID CIPROM/2021/017. Prof. Rosabel Roig is the chair of the UNESCO &#34;Education, Research and Digital Inclusion&#34;.

The following protocol follows the guidelines of the University of Alicante&#39;s human research ethics committee with the approved protocol number UA-2022-11-12. Informed consent has been obtained from all participants for this experiment and for using the data here.
1. Hardware, software, and class setup
<ol>
	<li>Set a router with WiFi capabilities (the experiments were carried out using a DLink DSR 1000AC) in the desired location so that its range covers the entire room. Here, 25 m2 classrooms with 30 students were covered.</li>
	<li>Set one smartwatch (here Samsung Galaxy Smartwatch 5) and one camera (here Logitech C920 cameras) for each student location. Set one embedded device for every two students. Fix two cameras on two tripods and connect them to another embedded device (hereafter referred to as zenithal cameras).</li>
	<li>Connect the cameras to the corresponding embedded devices with a USB link and turn them ON. Turn ON the smartwatches as well. Connect every embedded device and smartwatch to the WiFi network of the router set up in step 1.1.</li>
	<li>Place the cameras in the corner of each student&#39;s desk and point them forward and slightly tilted upwards so that the face of the student is clearly visible in the images.</li>
	<li>Place the two tripods with the cameras in front of the desks that are closest to the aisles in the first row of seats of the classroom. Move the tripod riser to the highest position so the cameras can clearly view most of the students. Each embedded device will be able to manage one or two students, along with their respective cameras and watches. The setup is depicted in Figure 1.</li>
	<li>Run the capturing software in the smartwatches and in the embedded devices so that they are ready to send the images and the accelerometer, gyroscope, heart rate, and lighting data. Run the server software that collects the data and stores it. A diagram of all these elements can be seen in Figure 2.</li>
	<li>Ensure the zenithal cameras dominate the scene so that they have a clear view of the students&#39; bodies. Place an additional individual camera in front of each student.</li>
	<li>Let the students sit in their places, inform them of the goals of the experiment, and instruct them to wear the smartwatches on their dominant hand and not to interact with any of the elements of the setup.</li>
	<li>Start the capture and collection of the data in the server and resume the class lessons as usual.</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65931/65931fig01.jpg" /> 
Figure 1: Hardware and data pipeline. The cameras and smartwatches data are gathered and fed to the machine learning algorithms to be processed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65931/65931fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65931/65931fig02.jpg" /> 
Figure 2: Position of the sensors, teacher, and students. Diagram showing the positions of the cameras, smartwatches, and GUI in the classroom with the teacher and students. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65931/65931fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Capture and data processing pipeline
NOTE: All these steps are performed automatically by processing software deployed in a server. The implementation used for the experiments in this work was written in Python 3.8.
<ol>
	<li>Collect the required data by gathering all the images and biometric data from the smartwatch for each student and build a data frame that includes data from 1 s. This data frame is composed of one image from the individual camera, one image from each zenithal camera (two in this case), fifty registers of the three gyroscope values, fifty registers of the three accelerometer values, one heart-rate value, and one light-condition value.</li>
	<li>To compute the head direction, the first step is to use the webcam that is pointed to the student&#39;s face and retrieve an image. Then, that image is processed by the BlazeFace17 landmark estimation algorithm. The output of such an algorithm is a list of 2D key points corresponding to specific areas of the face.</li>
	<li>By using the estimated position of the eyes, nose, mouth, and chin provided by the algorithm, solve the perspective n-point problem using the method cv::SolvePnPMethod of the OpenCV library, a canonical face setting. The rotation matrix that defines the head direction is obtained as a result of this procedure.</li>
	<li>Compute the body pose by forwarding the zenithal image from the data frame in which the student is depicted to the BlazePose18 landmark estimation algorithm and retrieve a list of 2D coordinates of the joints of the body depicted in the image. This list of landmarks describes the student&#39;s pose. 
	NOTE: The body pose is important as it can accurately represent a student&#39;s engagement with different activities during the class. For instance, if the student is sitting naturally with their hands on the desk, it could mean that they are taking notes. In contrast, if they are continually moving their arms, it could mean they are speaking to someone.</li>
	<li>Obtain the image of the student&#39;s face and perform alignment preprocessing. Retrieve the key points of the eyes from the list computed in step 2.3 and project them to the image plane so that the position of the eyes is obtained.</li>
	<li>Trace a virtual vector that joins the position of both eyes. Compute the angle between that vector and the horizontal line and build a homography matrix to apply the reverse angle to the image and the key points so that the eyes are aligned horizontally.</li>
	<li>Crop the face using the key points to detect its limits and feed the patch to the emotion detection convolutional neural network19. Retrieve the output from the network, which is a vector with 7 positions, each position giving the probability of the face showing one of the seven basic emotions: neutral, happy, disgusted, angry, scared, surprised, and sad.</li>
	<li>Measure the level of attention by obtaining the accelerometer, gyroscope, and heart rate data from the data frame (Figure 3).</li>
	<li>Feed the deep learning-based model that was custom-built and trained from scratch described in the representative results with the stream of accelerometer and gyroscope data. Retrieve the output from the model, which is a vector with 4 positions, each position giving the probability of the data representing one of the following possible actions: handwriting, typing on a keyboard, using a mobile phone, or resting, as shown in Figure 4.</li>
	<li>Feed the final attention classifier with a linearization of all the results from the previous systems by merging the head direction, emotion recognition output, body pose, and the gyroscope, accelerometer, and heart rate data. Retrieve the results from this final classifier, which is a score from 0 to 100. Classify this continuous value into one of the three possible discrete categories of attention: low level of attention (0-40%), medium level of attention (40%-75%), and high level of attention (75% - 100%). The structure of the dataset generated is shown in Table 1.</li>
	<li>Show the results of the attention levels to the teacher using the graphical user interface (GUI) from the teacher&#39;s computer, accessible from a regular web browser.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65931/65931fig03.jpg" /> 
Figure 3: Data captured by the smartwatch. The smartwatch provides a gyroscope, accelerometer, heart rate, and light condition as streams of data. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65931/65931fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65931/65931fig04.jpg" /> 
Figure 4: Examples of the categories considered by the activity recognition model. Four different actions are recognized by the activity recognition model: handwriting, typing on a keyboard, using a smartphone, and resting position. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65931/65931fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Enhancing Classroom Engagement: AI-Based Analysis of Student Attention Levels

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This work presents a system that measures the attention level of a student in a classroom using cameras, smartwatches, and artificial intelligence algorithms. This information is subsequently presented to the teacher for them to have an idea of the general state of the class.
One of the main critical steps of the protocol is the synchronization of the smartwatch information with the color camera image, as these have different frequencies. This was solved by deploying raspberries as servers tha...

In modern educational settings, accurately assessing and maintaining students&#39; attention is crucial for effective teaching and learning. However, traditional methods of gauging engagement, such as self-reporting or subjective teacher observations, are time-consuming and prone to biases. To address this challenge, Artificial Intelligence (AI) techniques have emerged as promising solutions for automated attention detection. One significant aspect of understanding students&#39; engagement levels is emotion recognition1. AI systems can analyze facial expressions to identify emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger2.
Gaze direction and body posture are also crucial indicators of students&#39; attention3. By utilizing cameras and advanced machine learning algorithms, AI systems can accurately track where students are looking and analyze their body posture to detect signs of disinterest or fatigue4. Furthermore, incorporating biometric data enhances the accuracy and reliability of attention detection5. By collecting measurements, such as heart rate and blood oxygen saturation levels, through smartwatches worn by students, objective indicators of attention can be obtained, complementing other sources of information.
This paper proposes a system that evaluates an individual&#39;s level of attention using color cameras and other different sensors. It combines emotion recognition, gaze direction analysis, body posture assessment, and biometric data to provide educators with a comprehensive set of tools for optimizing the teaching-learning process and improving student engagement. By employing these tools, educators can gain a comprehensive understanding of the teaching-learning process and enhance student engagement, thereby optimizing the overall educational experience. By applying AI techniques, it is even possible to automatically evaluate this data.
The main goal of this work is to describe the system that allows us to capture all the information and, once captured, to train an AI model that allows us to obtain the attention of the whole class in real-time. Although other works have already proposed capturing attention using visual or emotional information6, this work proposes the combined use of these techniques, which provides a holistic approach to allow the use of more complex and effective AI techniques. Moreover, the datasets hitherto available are limited to either a set of videos or one of biometric data. The literature includes no datasets that provide complete data with images of the student&#39;s face or their body, biometric data, data on the teacher&#39;s position, etc. With the system presented here, it is possible to capture this type of dataset.
The system associates a level of attention with each student at each point of time. This value is a probability value of attention between 0% and 100%, which can be interpreted as low level of attention (0%-40%), medium level of attention (40%-75%), and high level of attention (75%-100%). Throughout the text, this probability of attention is referred to as the level of attention, student attention, or whether students are distracted or not, but these are all related to the same output value of our system.
Over the years, the field of automatic engagement detection has grown significantly due to its potential to revolutionize education. Researchers have proposed various approaches for this area of study.
Ma et al.7 introduced a novel method based on a Neural Turing Machine for automatic engagement recognition. They extracted certain features, such as eye gaze, facial action units, head pose, and body pose, to create a comprehensive representation of engagement recognition.
EyeTab8, another innovative system, used models to estimate where someone is looking with both their eyes. It was specially made to work smoothly on a standard tablet with no modifications. This system harnesses well-known algorithms for processing images and analyzing computer vision. Their gaze estimation pipeline includes a Haar-like feature-based eye detector, as well as a RANSAC-based limbus ellipse fitting approach.
Sanghvi et al.9 propose an approach that relies on vision-based techniques to automatically extract expressive postural features from videos recorded from a lateral view, capturing the behavior of the children. An initial evaluation is conducted, involving the training of multiple recognition models using contextualized affective postural expressions. The results obtained demonstrate that patterns of postural behavior can effectively predict the engagement of the children with the robot.
In other works, such as&#160;Gupta et al.10, a deep learning-based method is employed to detect the real-time engagement of online learners by analyzing their facial expressions and classifying their emotions. The approach utilizes facial emotion recognition to calculate an engagement index (EI) that predicts two engagement states: engaged and disengaged. Various deep learning models, including Inception-V3, VGG19, and ResNet-50, are evaluated and compared to identify the most effective predictive classification model for real-time engagement detection.
In&#160;Altuwairqi et al.11, the researchers present a novel automatic multimodal approach for assessing student engagement levels in real-time. To ensure accurate and dependable measurements, the team integrated and analyzed three distinct modalities that capture students&#39; behaviors: facial expressions for emotions, keyboard keystrokes, and mouse movements.
Guill&#233;n et al.12 propose the development of a monitoring system that uses electrocardiography (ECG) as a primary physiological signal to analyze and predict the presence or absence of cognitive attention in individuals while performing a task.
Alban et al.13 utilize a neural network (NN) to detect emotions by analyzing the heart rate (HR) and electrodermal activity (EDA) values of various participants in both time and frequency domains. They find that an increase in the root-mean-square of successive differences (RMSDD) and the standard deviation normal-to-normal (SDNN) intervals, coupled with a decrease in the average HR, indicate heightened activity in the sympathetic nervous system, which is associated with fear.
Kajiwara et al.14 propose an innovative system that employs wearable sensors and deep neural networks to forecast the level of emotion and engagement in workers. The system follows a three-step process. Initially, wearable sensors capture and collect data on behaviors and pulse waves. Subsequently, time series features are computed based on the behavioral and physiological data acquired. Finally, deep neural networks are used to input the time series features and make predictions on the individual&#39;s emotions and engagement levels.
In other research, such as&#160;Costante et al.15, an approach based on a novel transfer metric learning algorithm is proposed, which utilizes prior knowledge of a predefined set of gestures to enhance the recognition of user-defined gestures. This improvement is achieved with minimal reliance on additional training samples. Similarly, a sensor-based human activity recognition framework16 is presented to address the goal of the impersonal recognition of complex human activities. Signal data collected from wrist-worn sensors is utilized in the human activity recognition framework developed, employing four RNN-based DL models (Long-Short Term Memories, Bidirectional Long-Short Term Memories, Gated Recurrent Units, and Bidirectional Gated Recurrent Units) to investigate the activities performed by the user of the wearable device.

<table><tbody><tr><td>4 GPUs&amp;nbsp; Nvidia A40 Ampere</td><td>NVIDIA</td><td>TCSA40M-PB</td><td>GPU for centralized model processing server</td></tr><tr><td>FusionServer 2288H V5</td><td>X-Fusion</td><td>02311XBK</td><td>Platform that includes power supply and motherboard for centralized model processing server</td></tr><tr><td>Memory Card Evo Plus 128 GB</td><td>Samsung</td><td>MB-MC128KA/EU</td><td>Memory card for the operation of the raspberry pi 4b 2gb.&amp;nbsp; One for each raspberry.&amp;nbsp;</td></tr><tr><td>NEMIX RAM - 512 GB Kit DDR4-3200 PC4-25600 8Rx4 EC</td><td>NEMIX</td><td>M393AAG40M32-CAE</td><td>RAM for centralized model processing server</td></tr><tr><td>Processor Intel Xeon Gold 6330</td><td>Intel</td><td>CD8068904572101</td><td>Processor for centralized model processing server</td></tr><tr><td>Raspberry PI 4B 2GB</td><td>Raspberry</td><td>1822095</td><td>Local server that receives requests from the clocks and sends them to the general server. One every two students.</td></tr><tr><td>Samsung Galaxy Watch 5 (40mm)</td><td>Samsung</td><td>SM-R900NZAAPHE</td><td>Clock that monitors each student&#039;s activity. For each student.&amp;nbsp;</td></tr><tr><td>Samsung MZQL23T8HCLS-00B7C PM9A3 3.84Tb Nvme U.2 PCI-Express-4 x4 2.5inch Ssd</td><td>Samsung</td><td>MZQL23T8HCLS-00B7C</td><td>Internal storage for centralized model processing server</td></tr><tr><td>WebCam HD Pro C920 Webcam FullHD</td><td>Logitech</td><td>960-001055</td><td>Webcam HD. One for each student plus two for student poses.</td></tr></tbody></table>

artificial intelligence-based system for detecting attention levels in students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information.
One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

The target group of this study is undergraduate and master&#39;s students, and so the main age group is between 18 and 25 years old. This population was selected because they can handle electronic devices with fewer distractions than younger students. In total, the group included 25 people. This age group can provide the most reliable results to test the proposal.
The results of the attention level shown to the teacher have 2 parts. Part A of the result shows individual information about the c...

Read this Scientific Article Protocol about Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention at JoVE.com

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

Artificial Intelligence-Based System for Detecting Attention Levels in Students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying ...

artificial-intelligence-based-system-for-detecting-attention-levels

Research

JoVE Journal

Engineering

2.4K Views.  University of Alicante. This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

Video: Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Methods to Test Visual Attention Online

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative data samples than can typically be collected on college campuses. However, before such methods can be widely adopted, a number of technological challenges must be overcome &#8211; in particular in experiments where tight control over stimulus properties is necessary. Here we present methods for collecting performance data on two tests of visual attention. Both tests require control over the visual angle of the stimuli (which in turn requires knowledge of the viewing distance, monitor size, screen resolution, etc.) and the timing of the stimuli (as the tests involve either briefly flashed stimuli or stimuli that move at specific rates). Data collected on these tests from over 1,700 online participants were consistent with data collected in laboratory-based versions of the exact same tests. These results suggest that with proper care, timing/stimulus size dependent tasks can be deployed in web-based settings.

To replicate laboratory settings, online data collection methods for visual tasks require tight control over stimulus presentation. We outline methods for the use of a web application to collect performance data on two tests of visual attention.

Online data collection methods have particular appeal to behavioral scientists because they offer the promise of much larger and much more representative ...

Behavior

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed.

This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners&#8217; engagement during a task.

In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris1 defined engagement by its multiple dimensions: ...

Light Enhanced Hydrofluoric Acid Passivation: A Sensitive Technique for Detecting Bulk Silicon Defects

A procedure to measure the bulk lifetime (&#62;100 &#181;sec) of silicon wafers by temporarily attaining a very high level of surface passivation when immersing the wafers in hydrofluoric acid (HF) is presented. By this procedure three critical steps are required to attain the bulk lifetime. Firstly, prior to immersing silicon wafers into HF, they are chemically cleaned and subsequently etched in 25% tetramethylammonium hydroxide. Secondly, the chemically treated wafers are then placed into a large plastic container filled with a mixture of HF and hydrochloric acid, and then centered over an inductive coil for photoconductance (PC) measurements. Thirdly, to inhibit surface recombination and measure the bulk lifetime, the wafers are illuminated at 0.2 suns for 1 min&#160;using a halogen lamp, the illumination is switched off, and a PC measurement is immediately taken. By this procedure, the characteristics of bulk silicon defects can be accurately determined. Furthermore, it is anticipated that a sensitive RT surface passivation technique will be imperative for examining bulk silicon defects when their concentration is low (&#60;1012 cm-3).

A RT liquid surface passivation technique to investigate the recombination activity of bulk silicon defects is described. For the technique to be successful, three critical steps are required: (i) chemical cleaning and etching of silicon, (ii) immersion of silicon in 15% hydrofluoric acid and (iii) illumination for 1 min.

A procedure to measure the bulk lifetime (&#62;100 &#181;sec) of silicon wafers by temporarily attaining a very high level of surface passivation when ...

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact with people around them. Head-mounted eye tracking offers a unique opportunity to capture children's dynamic egocentric views and how they allocate visual attention within those views. This protocol provides guiding principles and practical recommendations for researchers using head-mounted eye trackers in both laboratory and more naturalistic settings. Head-mounted eye tracking complements other experimental methods by enhancing opportunities for data collection in more ecologically valid contexts through increased portability and freedom of head and body movements compared to screen-based eye tracking. This protocol can also be integrated with other technologies, such as motion tracking and heart-rate monitoring, to provide a high-density multimodal dataset for examining natural behavior, learning, and development than previously possible. This paper illustrates the types of data generated from head-mounted eye tracking in a study designed to investigate visual attention in one natural context for toddlers: free-flowing toy play with a parent. Successful use of this protocol will allow researchers to collect data that can be used to answer questions not only about visual attention, but also about a broad range of other perceptual, cognitive, and social skills and their development.

Young children do not passively observe the world, but rather actively explore and engage with their environment. This protocol provides guiding principles and practical recommendations for using head-mounted eye trackers to record infants' and toddlers' dynamic visual environments and visual attention in the context of natural behavior.

Young children's visual environments are dynamic, changing moment-by-moment as children physically and visually explore spaces and objects and interact ...

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.

A protocol to evaluate solutions based on commercial-off-the-shelf (COTS) wrist wearables to estimate stress in students is proposed. The protocol is carried out in two phases, an initial laboratory-based stress induction test, and a monitoring stage taking place in the classroom while the student is performing academic activities.

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. ...

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Learning disabilities (LDs) encompass disorders of those who have difficulty learning and using academic skills, exhibiting performance below expectations for their chronological age in the areas of reading, writing, and/or mathematics. Each of the disorders making up the LDs involve different deficits; however, some commonalities can be found within that heterogeneity, such in terms of learning self-regulation and metacognition. Unlike in early ages and later educational levels, there are hardly any evidence-based evaluation protocols for adults with LDs. LDs influence academic performance but also have serious consequences in professional, social, and family contexts. In response to this, the current work proposes a multimodal evaluation protocol focused on metacognitive, self-regulation of learning, and emotional processes, which make up the basis of the difficulties in adults with LDs. The assessment is carried out through analysis of the on-line learning process using a variety methods, techniques, and sensors (e.g., eye tracking, facial expressions of emotion, physiological responses, concurrent verbalizations, log files, screen recordings of human-machine interactions) and off-line methods (e.g., questionnaires, interviews, and self-report measures). This theoretically-driven and empirically-based guideline aims to provide an accurate assessment of LDs in adulthood in order to design effective prevention and intervention proposals.

The current work proposes a multimodal evaluation protocol focused on metacognitive, self-regulation of learning, and emotional processes, which make up the basis of the difficulties in adults with LDs.

Learning disabilities (LDs) encompass disorders of those who have difficulty learning and using academic skills, exhibiting performance below expectations ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous technological changes and scientific advances, there is a need for life-long learning and education within both formal and non-formal educational environments. In response to this challenge, the use of eye-tracking technology and data-mining techniques, respectively, for supervised (mainly prediction) and unsupervised (specifically cluster analysis) learning, provide methods for the detection of forms of learning among users and/or the classification of their learning styles. In this study, a protocol is proposed for the study of learning styles among adults with and without previous knowledge at different ages (18 to 69-year-old) and at different points throughout the learning process (start and end). Statistical analysis-of-variance techniques mean that differences may be detected between the participants by type of learner and previous knowledge of the task. Likewise, the use of&#160;unsupervised learning clustering techniques throws light on similar forms of learning among the participants across different groups. All these data will facilitate personalized proposals from the teacher for the presentation of each task at different points in the chain of information processing. It will likewise be easier for the teacher to adapt teaching materials to the learning needs of each student or group of students with similar characteristics.

We present a protocol for a behavioral analysis of adults (ages 18 to 70-year-old) engaged in learning processes, undertaking tasks designed for Self-Regulated Learning (SRL). The participants, university teachers and students, and adults from the University of Experience, were monitored with eye-tracking devices and the data were analyzed with data-mining techniques.

Behavioral analysis of adults engaged in learning tasks is a major challenge in the field of adult education. Nowadays, in a world of continuous ...

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in scholastic, social and familial contexts. One of the most common problems in the identification of this disorder relates to the apparent over diagnosis of the disorder due to the absence of global protocols for assessment. The research group of School Learning, Difficulties and Academic Performance (ADIR) from the University of Oviedo, has developed a complete protocol that suggests the existence of certain patterns of cortical activation and executive control for identifying ADHD more objectively. This protocol takes into consideration some of the hypothetical determinants of ADHD, including the relationship between activation of selected areas of the brain, and differences in performance on various aspects of executive functioning such as omissions, commissions or response times, using innovative tools of Continuous Performance Testing (based on Virtual Reality CPT and Traditional CPT) and brain activation measures (two different tools, based on Hemoencephalography- nirHEG; and Quantified Electroencephalography --Q-EEG, respectively). This model of assessment aims to provide an effective assessment of ADHD symptomatology in order to design an accurate intervention and make appropriate recommendations for parents and teachers.

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

This work presents a new protocol for the assessment of Attention Deficit Hyperactivity Disorder (ADHD) by providing a more objective diagnostic procedure for this developmental disorder based on the use of innovative tools. It also analyzes the relationship between activation measures and executive function measures.

Attention Deficit Hyperactivity Disorder (ADHD) is a problem that impacts academic performance and has serious consequences that result in difficulties in ...

Neuroscience

The Attentional Blink

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
In order for recognition of a certain stimulus to take place, visual attention needs to be directed towards said stimulus. To the earliest parts of the visual system, objects are not objects, they are collections of visual features-lines, corners, changes in texture, color, and light. Attention is the resource that is necessary for later processing in order to recognize what a given bundle of features adds up to. This makes attention a central focus of research. One especially important set of questions concerns how people sustain attention, that is, the extent to which they can continuously maintain a focus of attention from moment-to-moment. It is now known that sustained attention takes great effort. When attention needs to be focused very rapidly on something that is moving or changing very quickly, the effort involved causes a momentary lapse in attention once it is disengaged. This kind of lapse in attention is called an attentional blink. It is like the brain blinks for a moment, shutting down attention for a rest. Stimuli that appear during an attentional blink will not be perceived. 
In 1992, a group of researchers devised a paradigm to study the attentional blink, and the paradigm has come to be known by the same name.1 It demonstrates some of the challenges to maintaining focused attention. This video demonstrates how to implement the attentional blink paradigm in order to study sustained visual attention.

Sustained Visual Attention and the Attentional Blink Paradigm

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
In order for recognition of a certain stimulus to take place, visual attention ...