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 proprietary eye-tracking, automatic facial emotion recognition, electrodermal activity and electroencephalography equipment and software.
1. Setup of the Lab Environment
<ol>
	<li>Turn on the eye-tracker, the EEG amplifier, the four recording computers and the speakers.</li>
	<li>Prepare the setup of the recording equipment:
	<ol>
		<li>Prepare the EEG setup with required material according to the manufacturer&#8217;s recommended procedures. Prepare the EEG software for the upcoming participant. Start the eye-tracking software and create a new participant profile in the software. Start the video recording software and the cameras.</li>
		<li>Start the synchronization software with the specific subroutine created for the project with markers at 60&#160;sec. Start the physiological measurement software (to record electrodermal activity) and open the specific layout created for the project. Adjust the participant&#8217;s chair to the highest level.</li>
	</ol>
	</li>
</ol>
2. Participant Preparation
<ol>
	<li>Ask the participant to read and sign the ethical consent form.</li>
	<li>Carry out skull measurements for EEG:
	<ol>
		<li>Find the Cz location on the participant&#8217;s head (according to 10 - 20 reference systems). Immerse the EEG net in the saline solution (potassium chloride) (see step 1.2.1) and start a timer (10 min) in accordance with the manufacturer&#8217;s standards.</li>
	</ol>
	</li>
	<li>Read the objective of the study and the steps in the experiment to the participant, &#8220;The objective of this study is to observe your brain activity while you answer physics problems. First we will install the sensors, then you will be asked to solve 10 Newtonian physics problems on the computer. We will ask you to take a 45-sec&#160;break after each problem with your eyes closed. After each problem, you will be asked to rate your assessment of the problem.&#8221;</li>
	<li>Tell the subject that the total duration of the experiment will be 90 min.</li>
	<li>Install the physiological sensors, according to the manufacturer&#8217;s recommendations: two gelified sensors on the top of the left hand.</li>
	<li>Install the EEG cap, according to the manufacturer&#8217;s recommendations and perform an impedance check with a threshold at 40 k&#937; (according to the manufacturer&#8217;s specifications).</li>
</ol>
3. Data Collection
<ol>
	<li>Make sure that all the recording software is ready to be started in synchrony:
	<ol>
		<li>Physiology (EDA data): Click the &#8220;start&#8221; button.</li>
		<li>Video recording: Click the &#8220;open&#8221; button.</li>
		<li>Eye-tracking: Click the &#8220;on hold&#8221; button.</li>
		<li>EEG: Click the &#8220;record&#8221; button.</li>
		<li>Synchronization software: Click the &#8220;green circle&#8221; button.</li>
	</ol>
	</li>
	<li>Eye gaze calibration:
	<ol>
		<li>Perform a five-point on-screen calibration and observe the participant while he/she follows the red dots (click &#8220;Tools/Settings/Calibration &#8230;&#8221;). Repeat this procedure until sufficient accuracy is achieved, according to the manufacturer&#8217;s standards.</li>
	</ol>
	</li>
	<li>Project task instructions onto the participant&#8217;s screen: ask if he/she has any questions after reading them, and if he/she is ready to start the experiment.</li>
	<li>Ask the participant to solve 10 Newtonian physics problems.</li>
	<li>If needed, perform an impedance check during one of the 45 s breaks (not before problem 5).</li>
	<li>Make sure the participant takes the full 45&#160;s break before each problem (to determine the baseline).</li>
</ol>
4. End of Data Collection
<ol>
	<li>Stop data acquisition on all computers and remove the sensors from the participant.</li>
</ol>
5. After the Participant Has Left
<ol>
	<li>Clean the EEG cap with germicide and tidy up the equipment, according to the manufacturer&#8217;s recommendations. Save all the data files collected and create a backup on the FTP server.</li>
	<li>Fill in the participant spreadsheet: note any particular event or problem during data collection. Erase all cookies from the web browser.</li>
</ol>
6. Data Pre-processing and Export to the Integration Software
<ol start="1">
	<li>EEG
	<ol>
		<li>Import EEG data into EEG data analysis software:
		<ol>
			<li>Create three empty folders on the computer named &#8220;Raw data&#8221;, &#8220;History&#8221; and &#8220;Export&#8221; to paste the raw EEG data into the newly created Raw data file.</li>
			<li>In the EEG data analysis software, click &#8220;File/New Project &#8230;&#8221; and choose the raw data location by clicking Browse, then selecting the newly created raw data file. Choose the location of the &#8220;History&#8221; and &#8220;Export&#8221; folders in the same way.</li>
			<li>Click &#8220;OK&#8221;. (The window should contain all the participant&#8217;s EEG data).</li>
		</ol>
		</li>
		<li>Pre-process the brain signal:
		<ol>
			<li>Apply a filter and a notch (click &#8220;Transformations/IIR filters&#8230;&#8221;). In the window, enable the low cutoff at 1.5&#160;Hz with a slope of 12 dB and the high cutoff at 50&#160;Hz with a slope of 12&#160;dB. Also enable a notch at 60 Hz frequency.</li>
			<li>Because a DC amplifier is used, DC detrend the signal (Click &#8220;Transformations/DC Detrend&#8230;&#8221; and enable &#8220;based on time&#8221; at 100&#160;msec before marker and 100&#160;msec before DC connection).</li>
			<li>Perform a raw data inspection (Click &#8220;Transformation/Raw data inspection&#8230;&#8221; and select semi-automatic artifact removal). Select the following: maximal voltage 60&#160;&#181;V/ms; Max-min: 200 &#181;V in 200&#160;ms interval; amplitude: -400 to +400 &#181;V).</li>
			<li>Perform an automatic ICA with classic sphering for eye blink removal (myographic artifacts do not need to be removed because their range is outside of the frequencies of interest). (Click &#8220;Transformations/ICA&#8230;&#8221;. At the end of the ICA, process the inverse ICA.)</li>
			<li>Re-reference (&#8220;Transformations/Re-reference&#8230;&#8221;) the signal and select &#8220;common average&#8221;.</li>
			<li>Export (click &#8220;Export/Generic Data Export&#8230;&#8221;) the signal and markers in text format (Select the &#8220;.vhdr&#8221; box) for an eventual Matlab construction of the engagement index. Also select the &#8220;Write header file&#8221; and &#8220;Write marker file&#8221; boxes.</li>
		</ol>
		</li>
		<li>Import the signal in Matlab.
		<ol>
			<li>Start Matlab and type &#8220;eeglab&#8221; so the GUI of EEGLab appears and Import the data for one participant at a time. In the GUI, select item menu &#8220;File/Import Data/Using EEGLab functions and plugins/From Brain Vis Rec .vhdr file&#8221;.</li>
			<li>In the command window, paste a script16 that generates an engagement index. &#160; 
			NOTE: The cognitive engagement script is computed by the average of each Beta/(Alpha + Theta) 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.</li>
		</ol>
		</li>
		<li>In MS Excel, open the text file of the engagement index that is generated at the end of the script by Matlab and apply a z-score normalization on EEG data to allow intersubject comparison. (For each value, compute this formula in Excel: Z=(value &#8211; overall mean)/overall standard deviation.)</li>
		<li>Save the z-score engagement index signal in a CSV file in MS Excel. (Click File/Save as &#8230; and select CSV in the format type.)</li>
		<li>Repeat the procedure (from step 6.1.2.2.) for each participant.</li>
	</ol>
	</li>
	<li>Physiology:
	<ol>
		<li>Import EDA data in physiological data analysis software.</li>
		<li>Apply these parameters to pre-process the physiological signal:
		<ol>
			<li>Apply a logarithmic transformation to normalize the distribution of the conductance as per Venables and Christie&#8217;s23 method.</li>
			<li>Flat the signal on a 10&#160;sec sliding window24.</li>
		</ol>
		</li>
		<li>Within the physiological software, compute a z-score normalization on the EDA data to allow intersubject comparison. (Z=(value &#8211; overall mean)/overall standard deviation).
		<ol>
			<li>Highlight all the data with the cursor from the EDA channel.</li>
			<li>In the top menu, select the EDA channel, and select &#8220;mean&#8221; to obtain the mean value of the overall channel. Also select the EDA channel and &#8220;stddev&#8221; to obtain the standard deviation value of the overall channel.</li>
			<li>To compute the z-score equation, click &#8220;Transformation/Waveform Math &#8230;&#8221; and select the EDA channel in Source 1 . Select &#8220;&#8211;&#8221; (minus) in the mathematical operation window and select K in source 2. Select &#8220;New destination&#8221; in the destination menu and enter the mean value of the EDA channel (see step 6.2.3.2). Select &#8220;Transform entire wave&#8221;, click OK and click &#8220;Transformation /Waveform Math &#8230;&#8221;. Select the EDA-K channel in source 1, select &#8220;/&#8221; (divide) in the mathematical operation window, select K in source 2, select &#8220;New destination&#8221; in destination and enter the standard deviation value of the EDA channel (step 6.2.3.2). Select &#8220;Transform entire wave&#8221; and click OK.</li>
		</ol>
		</li>
		<li>Export the signal (arousal) in a CSV file. (Click File/Save as &#8230; and select CSV in the format type.)</li>
	</ol>
	</li>
	<li>Automatic facial emotion recognition:
	<ol>
		<li>Import video data from the media recorder into automatic facial emotion recognition software. (Click &#8220;File/New&#8230;/Participant&#8230;&#8221;. After selecting a new participant in the project menu by clicking on it, click &#8220;File/New/Analysis/Video&#8230;&#8221;. Click the magnifying glass next to Analysis 1 and choose the desired video file.
		<ol>
			<li>Select an offline analysis for &#8220;every third frame&#8221; and activate &#8220;continuous calibration&#8221;.</li>
			<li>Export valence data in a CSV file. (Click &#8220;Options/Settings/Logging&#8230;&#8221;, check the &#8220;Write valence value to the log file&#8221; box. Click &#8220;File/Export&#8230;&#8221;, choose the location where the log files will be exported, and check the &#8220;Save detailed log&#8221; box.)</li>
			<li>Open the CSV file in MS Excel. Copy the valence data column in a single column of SPSS software. Click &#8220;Analyse/Descriptives Statistics/Descriptives&#8221; and select the just-pasted variable name. Check the box &#8220;Save standardized values in variables&#8221;. A column with a z-score will appear. Copy-paste these z-scores over the old data in the Excel file.</li>
		</ol>
		</li>
		<li>Save the Excel file with the z-scores of the signal (valence) in CSV format.</li>
	</ol>
	</li>
</ol>
7. Data Integration and Synchronization
<ol start="1">
	<li>In behavioral analysis software:
	<ol>
		<li>Import eye-tracking videos (behavioral engagement). (Click &#8220;File/Import/Video in a New Observation&#8230;&#8221;. Name the new observation and choose the desired video file.)</li>
		<li>Code each video with pertinent behaviors and contextual events (time markers, right/wrong answers).</li>
		<li>Import all external data with the appropriate header: z-score of EEG signal (cognitive engagement), z-score of EDA signal (emotional engagement), z-score of valence data (emotional engagement). (Click &#8220;File/Import/External Data&#8230;&#8221;. Select the appropriate file type and select the correct CSV file.)</li>
	</ol>
	</li>
	<li>Synchronize time between computers according to these formulas:
	<ol>
		<li>Time in eye gaze from time in EEG = Time in eye gaze + second marker in EEG &#8211; first marker in eye gaze.</li>
		<li>Time in eye gaze from time in facial emotion recognition = Time in eye gaze + first marker in facial emotion &#8211; first marker in eye gaze.</li>
		<li>Time in eye gaze from time in electrodermal activity = Time in eye gaze + first marker in electrodermal activity &#8211; first marker in eye gaze.</li>
	</ol>
	</li>
	<li>Enter the offset data by pressing &#8220;Ctrl + Shift + =&#8221;, to open the Offset menu. Select &#8220;Numerical offset&#8221; to enter the time in seconds between each pair of data sources [OK?]), according to the calculations above.</li>
	<li>Generate a report according to the variables of interest in the study.
	<ol>
		<li>Select interesting variables that will be generated in the report (click &#8220;Analyze/Select Data/New Profile Data&#8230;&#8221;). From the left, slide the desired variables between the &#8220;Start&#8221; box and the &#8220;Results&#8221; box, on the right.</li>
		<li>Generate the report. (Click &#8220;Analyze/Numerical Analysis/New&#8230;&#8221;, click &#8220;Statistics&#8221; and check the mean box in the external data menu. Finish by clicking &#8220;Calculate&#8221;.)</li>
	</ol>
	</li>
	<li>Export the data into statistical analysis software and perform analysis according to the study objectives.</li>
</ol>

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The authors have nothing to disclose.

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...

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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: ...

assessing-multiple-dimensions-engagement-to-characterize-learning

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JoVE Journal

Behavior

12.4K Views.  Université du Québec à Montréal. This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task. 

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

The Use of the Puzzle Box as a Means of Assessing the Efficacy of Environmental Enrichment

Environmental enrichment can dramatically influence the development and function of neural circuits. Further, enrichment has been shown to successfully delay the onset of symptoms in models of Huntington&#8217;s disease 1-4, suggesting environmental factors can evoke a neuroprotective effect against the progressive, cellular level damage observed in neurodegenerative disorders. The ways in which an animal can be environmentally enriched, however, can vary considerably. Further, there is no straightforward manner in which the effects of environmental enrichment can be assessed: most methods require either fairly complicated behavioral paradigms and/or postmortem anatomical/physiological analyses. This protocol describes the use of a simple and inexpensive behavioral assay, the Puzzle Box 5-7 as a robust means of determining the efficacy of increased social, sensory and motor stimulation on mice compared to cohorts raised in standard laboratory conditions. This simple problem solving task takes advantage of a rodent&#8217;s innate desire to avoid open enclosures by seeking shelter. Cognitive ability is assessed by adding increasingly complex impediments to the shelter&#8217;s entrance. The time a given subject takes to successfully remove the obstructions and enter the shelter serves as the primary metric for task performance. This method could provide a reliable means of rapidly assessing the efficacy of different enrichment protocols on cognitive function, thus paving the way for systematically determining the role specific environmental factors play in delaying the onset of neurodevelopmental and neurodegenerative disease.

Environmental enrichment provides a potential protective effect against neurodegenerative disorders. Currently, however, there is no easy way of determining the efficacy of enrichment procedures. This protocol describes a simple &#8220;Puzzle Box&#8221; method for assessing an animal&#8217;s cognitive function, in order to reveal the effectiveness of environmental enrichment.

Environmental enrichment can dramatically influence the development and function of neural circuits.  Further, enrichment has been shown to successfully ...

Mindfulness in Motion (MIM): An Onsite Mindfulness Based Intervention (MBI) for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

A pragmatic mindfulness intervention to benefit personnel working in chronically high-stress environments, delivered onsite during the workday, is timely and valuable to employee and employer alike. Mindfulness in Motion (MIM) is a Mindfulness Based Intervention (MBI) offered as a modified, less time intensive method (compared to Mindfulness-Based Stress Reduction), delivered onsite, during work, and intends to enable busy working adults to experience the benefits of mindfulness. It teaches mindful awareness principles, rehearses mindfulness as a group, emphasizes the use of gentle yoga stretches, and utilizes relaxing music in the background of both the group sessions and individual mindfulness practice. MIM is delivered in a group format, for 1 hr/week/8 weeks. CDs and a DVD are provided to facilitate individual practice. The yoga movement is emphasized in the protocol to facilitate a quieting of the mind. The music is included for participants to associate the relaxed state experienced in the group session with their individual practice. To determine the intervention feasibility/efficacy we conducted a randomized wait-list control group in Intensive Care Units (ICUs). ICUs represent a high-stress work environment where personnel experience chronic exposure to catastrophic situations as they care for seriously injured/ill patients. Despite high levels of work-related stress, few interventions have been developed and delivered onsite for such environments. The intervention is delivered on site in the ICU, during work hours, with participants receiving time release to attend sessions. The intervention is well received with 97% retention rate. Work engagement and resiliency increase significantly in the intervention group, compared to the wait-list control group, while participant respiration rates decrease significantly pre-post in 6/8 of the weekly sessions. Participants value institutional support, relaxing music, and the instructor as pivotal to program success. This provides evidence that MIM is feasible, well accepted, and can be effectively implemented in a chronically high-stress work environment.

Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

The Mindfulness in Motion (MIM) protocol offers a pragmatic Mindfulness Based Intervention (MBI) on-site, for persons working in chronically high-stress work environments that significantly increases resiliency and work engagement. The protocol has proven feasible, beneficial, and is easily adaptable to other high-stress workplaces.

A pragmatic mindfulness intervention to benefit personnel working in chronically high-stress environments, delivered onsite during the workday, is timely ...

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Extinction describes the process of attenuating behavioral responses to neutral stimuli when they no longer provide the reinforcement that has been maintaining the behavior. There is close correspondence between fear and human anxiety, and therefore studies of extinction learning might provide insight into the biological nature of anxiety-related disorders such as post-traumatic stress disorder, and they might help to develop strategies to treat them. Preclinical research aims to aid extinction learning and to induce targeted plasticity in extinction circuits to consolidate the newly formed memory. Vagus nerve stimulation (VNS) is a powerful approach that provides tight temporal and circuit-specific release of neurotransmitters, resulting in modulation of neuronal networks engaged in an ongoing task. VNS enhances memory consolidation in both rats and humans, and pairing VNS with exposure to conditioned cues enhances the consolidation of extinction learning in rats. Here, we provide a detailed protocol for the preparation of custom-made parts and the surgical procedures required for VNS in rats. Using this protocol we show how VNS can facilitate the extinction of conditioned fear responses in an auditory fear conditioning task. In addition, we provide evidence that VNS modulates synaptic plasticity in the pathway between the infralimbic (IL) medial prefrontal cortex and the basolateral complex of the amygdala (BLA), which is involved in the expression and modulation of extinction memory.

Vagus nerve stimulation (VNS) has emerged as a tool to induce targeted synaptic plasticity in the forebrain to modify a range of behaviors. This protocol describes how to implement VNS to facilitate the consolidation of fear extinction memory.

Extinction describes the process of attenuating behavioral responses to neutral stimuli when they no longer provide the reinforcement that has been ...

Place and Response Learning in the Open-field Tower Maze

This protocol describes how the Open-field Tower Maze (OFTM) paradigm is used to study spatial learning in rodents. This maze is especially useful for examining how rats learn to use a place- or response-learning to successfully navigate in an open-field arena. Additionally, this protocol describes how the OFTM differs from other behavioral maze paradigms that are commonly used to study spatial learning in rodents. The OFTM described in this article was adapted from the one previously described by Cole, Clipperton, and Walt (2007). Specifically, the OFTM was created to test spatial learning in rodents without the experimenter having to consider how &#8220;stress&#8221; might play a role as a confounding variable. Experiments have shown that stress-alone can significantly affect cognitive function1. The representative results section contains data from an experiment that used the OFTM to examine the effects of estradiol treatment on place- and response-learning in adult female Sprague Dawley rats2. Future studies will be designed to examine the role of the hippocampus and striatum in place- and response-learning in the OFTM.

The Open-field Tower Maze (OFTM) is a maze created to study the behavioral and neural mechanisms of spatial learning (e.g., place- or response-learning) in rats. This maze is especially useful for experimenters who want to use a non-stressful maze paradigm to investigate spatial learning in their research.

This protocol describes how the Open-field Tower Maze (OFTM) paradigm is used to study spatial learning in rodents. This maze is especially useful for ...

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. ...

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 appearance and behavior of visual stimuli, compared to manipulating live animals. Here, we present the FishSim Animation Toolchain, a software framework developed to provide researchers with an easy-to-use method for implementing 3D computer animations in behavioral experiments with fish. The toolchain offers templates to create virtual 3D stimuli of five different fish species. Stimuli are customizable in both appearance and size, based on photographs taken of live fish. Multiple stimuli can be animated by recording swimming paths in a virtual environment using a video game controller. To increase standardization of the simulated behavior, the prerecorded swimming path may be replayed with different stimuli. Multiple animations can later be organized into playlists and presented on monitors during experiments with live fish.
In a case study with sailfin mollies (Poecilia latipinna), we provide a protocol on how to conduct a mate-choice copying experiment with FishSim. We utilized this method to create and animate virtual males and virtual model females, and then presented these to live focal females in a binary choice experiment. Our results demonstrate that computer animation may be used to simulate virtual fish in a mate-choice copying experiment to investigate the role of female gravid spots as an indication of quality for a model female in mate-choice copying.
Applying this method is not limited to mate-choice copying experiments but can be used in various experimental designs. Still, its usability depends on the visual capabilities of the study species and first needs validation. Overall, computer animations offer a high degree of control and standardization in experiments and bear the potential to &#39;reduce&#39; and &#39;replace&#39; live stimulus animals as well as to &#39;refine&#39; experimental procedures.

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

Using the novel FishSim Animation Toolchain, we present a protocol for non-invasive visual manipulation of public information in the context of mate-choice copying in sailfin mollies. FishSim Animation Toolchain provides an easy-to-use framework for the design, animation and presentation of computer-animated fish stimuli for behavioral experiments with live test fish.

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 disorders and associated developmental disabilities. Similarly, a verbal operant analysis carefully controls specific environmental factors that influence language, and measures strength of responding across four verbal operant classes: tact, mand, echoic, and sequelic. The frequency of each independent verbal operant is then measured against one another using the stimulus control ratio equation (SCoRE) to summarize the relative strength of the speaker&#8217;s repertoire. The verbal behavior SCoRE yields a statistic that can be compared against itself (e.g., for the purposes of pre/post testing) or against others (e.g., for the purposes of randomized controlled trials). The results of this evaluation provide an individualized hierarchy for diverging stimulus control across the verbal operants, from which a treatment plan for errorless language learning may be prescribed.

This article describes the procedures for conducting a verbal operant analysis (VOA), calculating the stimulus control ratio equation (SCoRE), and developing individualized verbal behavior treatment plans, specifically significant to address the deficits characteristic of autism.

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

An Instrumented Pull Test to Characterize Postural Responses

Impairment of postural reflexes, termed postural instability, is a common and disabling deficit in Parkinson's disease. To assess postural reflexes, clinicians typically employ the pull test to grade corrective responses to a backward perturbation at the shoulders. However, the pull test is prone to issues with reliability and scaling (score/4). Here, we present an instrumented version of the pull test to more precisely quantify postural responses. Akin to the clinical test, pulls are manually administered except pull force is also recorded. Displacements of the trunk and feet are captured by a semi-portable motion tracking system. Raw data represent distance traveled (in millimeter units), making subsequent interpretation and analysis intuitive. The instrumented pull test also detects variabilities influencing pull test administration, such as pull force, thereby identifying and quantifying potential confounds that can be accounted for by statistical techniques. The instrumented pull test could have application in studies seeking to capture early abnormalities in postural responses, track postural instability over time, and detect responses to therapy.

Impairment of postural reflexes, termed postural instability, is difficult to quantify. Clinical assessments such as the pull test suffer issues with reliability and scaling. Here, we present an instrumented version of the pull test to objectively characterize postural responses.

Impairment of postural reflexes, termed postural instability, is a common and disabling deficit in Parkinson's disease. To assess postural reflexes, ...

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 ...

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

Summary

Explore More Videos

Chapters in this video

The Use of the Puzzle Box as a Means of Assessing the Efficacy of Environmental Enrichment

Mindfulness in Motion (MIM): An Onsite Mindfulness Based Intervention (MBI) for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Vagus Nerve Stimulation as a Tool to Induce Plasticity in Pathways Relevant for Extinction Learning

Place and Response Learning in the Open-field Tower Maze

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

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

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

An Instrumented Pull Test to Characterize Postural Responses

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