Name: assessing the multiple dimensions of engagement to characterize learning: a neurophysiological perspective
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The authors acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada (SSHERC), Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche Nature et Technologies du Qu&#233;bec (FQRNT) and Fonds de Recherche sur la Soci&#233;t&#233; et Culture du Qu&#233;bec (FQRSC).

This protocol received an ethical certificate from the Comit&#233; institutionnel de la recherche avec des &#234;tres humains (CIER) de l&#8217;Universit&#233; du Qu&#233;bec &#224; Montr&#233;al (UQAM) that was endorsed by HEC-Montreal for the Tech3Lab research facility. The protocol describes each of the specific steps that are performed in our lab environment and equipment. Although precise software paths are provided to clarify the methodology, this technique is transferable and can be replicated with other proprieta...

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In terms of critical steps within the protocol, it should first be pointed out that data quality is always the main focus for neurophysiological collection techniques. In this methodology, research assistants must pay special attention to instructing the subjects to minimize head movements that will interfere with valence monitoring (losing correct face angle for the camera) or generate myographic artifacts in the EEG. On the other hand, a balance must be maintained between the authenticity of real problem solving and in...

Engagement plays a crucial role in learning. For Clark and Mayer2, &#8220;all learning requires engagement,&#8221; regardless of delivery media. Zhang et al.3 also suggested that increased student engagement can improve learning outcomes, such as problem solving and critical thinking skills. Defining engagement remains a challenge. In their literature review, Fredricks, Blumenfeld and Paris1 defined engagement by its multifaceted nature: &#8220;Behavioural engagement draws on the idea of participation; it includes involvement in academic and social or extracurricular activities. (&#8230;) Emotional engagement encompasses positive and negative reactions to teachers, classmates, academics, and school and is presumed to create ties to an object and influence willingness to do the work. Finally, cognitive engagement draws on the idea of mental investment; it incorporates thoughtfulness and willingness to exert the effort necessary to comprehend complex ideas and master difficult skills.&#8221;
Fredricks, Blumenfeld and Paris1 also claimed that a focus on behavior, emotion, and cognition, within the concept of engagement, may provide a richer characterization of learning. These authors pointed out that a robust body of research addresses each component of engagement separately, but these components had not been studied in conjunction. They also observed that little information is available about interactions between the dimensions and that more studies could contribute to planning finely tuned teaching interventions. As a step in that direction, this paper describes a research methodology that was developed to gather and analyze quantitative and qualitative data, synchronously, on behavioral, emotional and cognitive engagement during learning tasks.
Bringing the Neurosciences into Education
Behavior, and consequently behavioral engagement, has long been the central focus of studies in education: research designs focused mainly on changes in knowledge and behavior occurring over long periods of time, between pre- and post-tests, and over intervals of hours, weeks, months or years. Discriminating between behavioral, emotional, and cognitive engagement remains a challenge because the last two dimensions are not systematically observable externally. Cognition and emotions must either be inferred from observations or assessed with self-report measures. From an external point of view, it remains difficult to determine whether students are trying to get their work done as quickly as possible or using deep-level learning strategies to master a specific content. In point of fact, Fredricks, Blumenfeld and Paris1 were unable to find any published studies using direct, objective measures of cognitive engagement.
Recent technological developments in the field of neurosciences have created new possibilities for research in education. New data collection methods and analysis algorithms developed in the field of neuroergonomics seem very promising for qualitative and quantitative studies during learning tasks. Other disciplines, such as economics, psychology, marketing, and ergonomics, have been using neurophysiological measurements to assess cognitive engagement for some time4-8. Neurophysiological measures, coupled with efficient analysis algorithms, allow one to study a phenomenon without disturbing it. By their nature, self-report questionnaires disengage students from learning. Neurophysiological measures allow research designs to be carried out in more authentic learning environments. These tools include equipment to monitor heart rate, breathing rate, blood pressure, body temperature, pupil diameter, electrodermal activity, electroencephalography (EEG), etc.
Gathering Synchronized Data on Behavioral, Emotional, and Cognitive Engagement
As representative outcomes following the use of this protocol, this paper will present partial results of a study in which learners had to solve, on a computer screen, ten problems in mechanical physics. These problems were developed in previous work9. Neurophysiological data were collected while the learners were solving the problems and relaxing during a 45&#160;s break, with their eyes closed, after each problem.
As mentioned above, behavioral engagement data consist of software interactions (mouse movements and clicks), eye gaze, performance and answers to questions produced by a learner interacting with the system while accomplishing the task1. An eye-tracking system was used to collect software interactions and eye gaze data. Performance data (time to solve a problem, correctness of answers) were collected on a survey website that was used to present the task. This website was also used to gather self-report data collected with a questionnaire adapted from Bradley and Lang10. Emotional engagement involves characterization of emotions. According to Lang11, emotions are characterized in terms of valence (pleasant/unpleasant) and arousal (calm/aroused). Emotional engagement data were accordingly collected, using automatic facial emotion recognition software that quantifies emotional valence and an electrodermal activity encoder/sensor for arousal12,13. Electrodermal activity (EDA) refers to the recorded electrical resistance between two electrodes when a very weak electrical current is steadily passed between them. Cacioppo, Tassinary and Berntson14 showed that the resistance recorded varies according to the subject&#8217;s arousal. Thus, psychophysiological data, such as valence or arousal, are considered as correlates of emotional engagement.
Finally, cognitive engagement data are collected through electroencephalography (EEG). EEG measures, on the scalp, the synchronized electrical activity of groups of neurons in the brain. Electrical signals recorded from the scalp are often oscillatory and composed of frequency components. By convention, these frequencies are grouped in sequences, known as bands. For example, alpha, beta and theta bands are the focus of this study. According to neuroscientific studies14, these bands reflect different cognitive processing abilities in specific areas of the brain. Thus, the analysis of the power spectral density (PSD) of specific frequencies, combined with numerous studies7,15 on alertness and attention, allows researchers to quantify cognitive engagement during a task. As Mikulka et al.16 noted, research has shown a direct relationship between beta activity and cognitive alertness and an indirect relationship between alpha and theta activity and alertness. Thus, Pope, Bogart and Bartoleme7 developed an engagement index that computes the PSD of three bands: beta / (alpha + theta). This ratio was validated in other studies on engagement16,17,18. To characterize cognitive engagement over time, a fast Fourier transform (FFT) converts the EEG signal from each active site (F3, F4, O1, O2) into a power spectrum. The EEG engagement index at time T is computed by the average of each engagement ratio within a 20&#160;sec&#160;sliding window preceding time T. This procedure is repeated every second and a new sliding window is used to update the index.
Since the aim of this methodology is to provide a rich analysis of the multiple dimensions of engagement, data synchronization is crucial. As Leger et al.19 remind readers, equipment manufacturers strongly recommend using only one computer per measurement tool to guarantee their specified precision level. Thus, when multiple computers are employed, synchronization between recording computers becomes a critical step. The recordings cannot all be started at the exact same time, and each data stream has its specific time frame (e.g., sec&#160;0 of eye tracking &#8800; sec&#160;0 of EEG or physiological data). This is extremely important: desynchronization between data streams means errors in the quantification of each dimension of engagement. There are different ways of synchronizing concurrent physiological and behavioral recordings. These methods may be divided into two main approaches; direct and indirect20. The protocol presented in the next section is based on an indirect approach where an external device, a syncbox, is used to send transistor-transistor logic (TTL) signals to all the recording equipment (as shown in Figure 1). As each piece of equipment has a different start time, the TTL markers are recorded in the log files with a relative delay. Markers are then used to realign the signals and thus ensure proper synchronization after each recording. A behavioral analysis software program that allows external file integration is used to re-synchronize the timeline of each data stream and to perform quantitative and qualitative analysis of each dimension of engagement.
<img alt="Figure 1" src="/files/ftp_upload/52627/52627fig1.jpg" /> 
Figure 1. Architecture of the Data Collection System. The lab environment in which behavioral (eye-tracking), emotional (EDA and facial emotion) and cognitive (EEG) engagement data are collected contains many computers. This raises a synchronization challenge for data that are referenced on their respective computer clocks. To be able to analyze all data in the same reference time, the lab setup involves a syncbox that sends TTL signals to all data streams. <a href="https://www.jove.com/files/ftp_upload/52627/52627fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To evaluate the precision of the methodology in terms of synchronization, 45&#160;sec&#160;pauses were introduced before each of the mechanical physics problems. During these pauses, subjects had to relax and to close their eyes. As seen in other studies4,9,16,17,18, these pauses should induce significant variations in the collected signal: the two eye pupil dots in eye-tracking immediately disappear (behavioral engagement) and an immediate drop in cognitive engagement (EEG signal) is observed. These specific components of the signal are used to evaluate the general validity of the synchronization. The recent publication of papers that fully or partially rely on this synchronization procedure, in the fields of information systems19, human-machine interactions21 and education9, 22, provides evidence of its effectiveness.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>EGI GSN-32&#160;</td>
			<td>EGI</td>
			<td>n/a</td>
			<td>Dense array EEG</td>
		</tr>
		<tr>
			<td>Netstation v.5.0</td>
			<td>EGI</td>
			<td>n/a</td>
			<td>EEG data collection software: EEG is collected with 32-electrode dense array electroencephalography (dEEG) geodesic sensor net using Netstation acquisition software and EGI amplifiers (Electrical Geodesics, Inc). The vertex (recording site Cz) is the reference electrode for recording. Impedance is kept below 50 k&#937; with a sampling rate of 250 Hz.&#160;</td>
		</tr>
		<tr>
			<td>Facereader v.4</td>
			<td>Noldus</td>
			<td>n/a</td>
			<td>Facial emotion recognition software</td>
		</tr>
		<tr>
			<td>Syncbox</td>
			<td>Noldus</td>
			<td>n/a</td>
			<td>Syncbox start the co-registration of EEG and gaze data by sending a Transistor-Transistor Logic (TTL) signal to the EGI amplifier and a keystroke signal to the Tobii Studio v 3.2.</td>
		</tr>
		<tr>
			<td>Logitech C600&#160; Webcam</td>
			<td></td>
			<td>960-000396</td>
			<td>Webcam used to gather video data sent to mediarecorder and that will be analyzed in Facereader</td>
		</tr>
		<tr>
			<td>The Observer XT</td>
			<td>Noldus</td>
			<td>n/a</td>
			<td>Integration and synchronization software: The Noldus Observer XT (Noldus Information Technology) is used to synchronize all behavioral, emotional and cognitive engagement data.&#160;</td>
		</tr>
		<tr>
			<td>On-Screen LED illumination</td>
			<td>Noldus</td>
			<td>n/a</td>
			<td>Neon positioned on computer screen in order to correctly light the face of subjects</td>
		</tr>
		<tr>
			<td>MediaRecorder</td>
			<td>Noldus</td>
			<td>n/a</td>
			<td>Video data collection software</td>
		</tr>
		<tr>
			<td>Tobii X60</td>
			<td>Tobii</td>
			<td>n/a</td>
			<td>Collect eye-movement patterns :&#160; used to record subjects&#8217; eye movement patterns at 60Hz during the experiment.&#160;</td>
		</tr>
		<tr>
			<td>Tobii Studio v.3.2</td>
			<td>Tobii</td>
			<td>n/a</td>
			<td>Eye-tracking data collection and analysis software</td>
		</tr>
		<tr>
			<td>Analyzer 2</td>
			<td>Brainvision</td>
			<td>n/a</td>
			<td>EEG signal processing software</td>
		</tr>
		<tr>
			<td>Acqknowledge v.4.0</td>
			<td>Biopac</td>
			<td>ACK100M</td>
			<td>Physiological signal acquisition and processing software</td>
		</tr>
		<tr>
			<td>Control III germicide solution</td>
			<td>Maril Products.</td>
			<td>10002REVA-20002-1</td>
			<td>Disinfectant solution used with EEG helmets : recommended by EGI</td>
		</tr>
		<tr>
			<td>Unipark</td>
			<td>QuestBack AG</td>
			<td>n/a</td>
			<td>Online survey environment</td>
		</tr>
	</tbody>
</table>

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.

Figures 2 and 3 show screenshots of the results of the integration and synchronization of behavioral, emotional and cognitive engagement data in a behavioral analysis software application. In both figures, the left-hand section organizes the research subjects and the coding scheme. In the middle section, a video (with red dots) shows the subject&#8217;s eye gaze during the task. The subject&#8217;s behavioral engagement can be inferred based on what he/she is looking at during the task and what actions a...

Watch this Scientific Journal Video about Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective at JoVE.com

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

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: ...

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This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Over the last decade, employing computer animations for animal behavior research has increased due to its ability to non-invasively manipulate the ...

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

Verbal operant analyses are the extension of functional analysis technology to the field of verbal behavior, of particular relevance to autism spectrum ...