Name: eye-tracking technology and data-mining techniques used for a behavioral analysis of adults engaged in learning processes
Uploaded: 2021-06-10T14:00:00
Description: Watch this Scientific Journal Video about Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes at JoVE.com

The work has been developed within the Project &#34;Self-Regulated Learning in SmartArt Erasmus+ Adult Education&#34; 2019-1-ES01-KA204-095615-Coordinator 6, funded by the European Commission. The video of the task completion phase had the prior informed consent of Rut Velasco S&#225;iz. We appreciate the participation of teachers and students in the task implementation phase.

This protocol was performed in compliance with the procedural regulations of the Bioethical Committee of the University of Burgos (Spain) n&#186; N&#186; IR27/2019. Prior to their participation, the participants had been made fully aware of the research objectives and had all provided their informed consent. They received no financial compensation for their participation.
1. Participant recruitment
<ol>
	<li>Recruit participants from among a group of adults within two environments (students ...

<ol>
	<li>van Marlen, T., van Wermeskerken, M., Jarodzka, H., van Gog, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effectiveness+of+eye+movement+modeling+examples+in+problem+solving:+The+role+of+verbal+ambiguity+and+prior+knowledge.">Effectiveness of eye movement modeling examples in problem solving: The role of verbal ambiguity and prior knowledge.</a> Learning and Instruction. 58, 274-283 (2018).</li><li>Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., Lester, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+sequence+mining+to+reveal+the+efficiency+in+scientific+reasoning+during+STEM+learning+with+a+game-based+learning+environment.">Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment.</a> Learning and Instruction. 54, 93-103 (2018).</li><li>Cloude, E. B., Taub, M., Lester, J., Azevedo, R., Isotani, S., Millan, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Role+of+Achievement+Goal+Orientation+on+Metacognitive+Process+Use+in+Game-Based+Learning.">The Role of Achievement Goal Orientation on Metacognitive Process Use in Game-Based Learning.</a> Artificial Intelligence in Education. , 36-40 (2019).</li><li>Azevedo, R., Ga&#353;evi&#263;, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+Multimodal+Multichannel+Data+about+Self-Regulated+Learning+with+Advanced+Learning+Technologies:+Issues+and+Challenges.">Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges.</a> Computers in Human Behavior. 96, 207-210 (2019).</li><li>S&#225;iz-Manzanares, M. C., Rodr&#237;guez-Diez, J. J., Marticorena-S&#225;nchez, R., Zapar&#237;n-Y&#225;&#241;ez, M. J., Cerezo-Men&#233;ndez, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lifelong+learning+from+sustainable+education:+An+analysis+with+eye+tracking+and+data+mining+techniques.">Lifelong learning from sustainable education: An analysis with eye tracking and data mining techniques.</a> Sustainability. 12 (5), 2-18 (2020).</li><li>Alemdag, E., Cagiltay, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+systematic+review+of+eye+tracking+research+on+multimedia+learning.">A systematic review of eye tracking research on multimedia learning.</a> Computers &amp; Education. 125, 413-428 (2018).</li><li>Ho, H. N. J., Tsai, M. -. J., Wang, C. -. Y., Tsai, C. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prior+knowledge+and+online+inquiry-based+science+reading:+evidence+from+eye+tracking.">Prior knowledge and online inquiry-based science reading: evidence from eye tracking.</a> International Journal of Science and Mathematics Education. 12, 525-554 (2014).</li><li>Catrysse, L., Gijbels, D., Donche, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=It+is+not+only+about+the+depth+of+processing:+What+if+eye+am+not+interested+in+the+text.">It is not only about the depth of processing: What if eye am not interested in the text.</a> Learning and Instruction. 58, 284-294 (2018).</li><li>Mayer, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+multimedia+for+e-learning.">Using multimedia for e-learning.</a> Journal of Computer Assisted Learning. 33 (5), 403-423 (2017).</li><li>Prokop, M., Pila&#345;, L., Tich&#225;, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+think-aloud+on+eye-tracking:+A+comparison+of+concurrent+and+retrospective+think-aloud+for+research+on+decision-making+in+the+game+environment.">Impact of think-aloud on eye-tracking: A comparison of concurrent and retrospective think-aloud for research on decision-making in the game environment.</a> Sensors. 20 (10), (2020).</li><li>DuMouchel, W., Abello, J., Pardalos, P. M., Resende, M. G. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+Squashing:+Constructing+Summary+Data+Sets.">Data Squashing: Constructing Summary Data Sets.</a> Handbook of Massive Data Sets. , 579-591 (2002).</li><li>Stull, A. T., Fiorella, L., Mayer, R. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+eye-tracking+analysis+of+instructor+presence+in+video+lectures.">An eye-tracking analysis of instructor presence in video lectures.</a> Computers in Human Behavior. 88, 263-272 (2018).</li><li>K&#246;nig, S. D., Buffalo, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+nonparametric+method+for+detecting+fixations+and+saccades+using+cluster+analysis:+Removing+the+need+for+arbitrary+thresholds.">A nonparametric method for detecting fixations and saccades using cluster analysis: Removing the need for arbitrary thresholds.</a> Journal of Neuroscience Methods. 30 (227), 121-131 (2014).</li><li>Maltz, M., Shinar, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eye+Movements+of+Younger+and+Older+Drivers.">Eye Movements of Younger and Older Drivers.</a> Human Factors. 41 (1), 15-25 (1999).</li><li>Burch, M., Kull, A., Weiskopf, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AOI+rivers+for+visualizing+dynamic+eye+gaze+frequencies.">AOI rivers for visualizing dynamic eye gaze frequencies.</a> Computer Graphics Forum. 32 (3), 281-290 (2013).</li><li>Dzeng, R. -. J., Lin, C. -. T., Fang, Y. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+eye-tracker+to+compare+search+patterns+between+experienced+and+novice+workers+for+site+hazard+identification.">Using eye-tracker to compare search patterns between experienced and novice workers for site hazard identification.</a> Safety Science. 82, 56-67 (2016).</li><li>Kurzhals, K., Weiskopf, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Space-time+visual+analytics+of+eye-tracking+data+for+dynamic+stimuli.">Space-time visual analytics of eye-tracking data for dynamic stimuli.</a> IEEE Transactions on Visualization and Computer Graphics. 19 (12), 2129-2138 (2013).</li><li>Sharafi, Z., Soh, Z., Gu&#233;h&#233;neuc, Y. -. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+systematic+literature+review+on+the+usage+of+eye-tracking+in+software+engineering.">A systematic literature review on the usage of eye-tracking in software engineering.</a> Information and Software Technology. 67, 79-107 (2015).</li><li>Dalrymple, K. A., Jiang, M., Zhao, Q., Elison, J. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Machine+learning+accurately+classifies+age+of+toddlers+based+on+eye+tracking.">Machine learning accurately classifies age of toddlers based on eye tracking.</a> Scientific Reports. 9 (1), 6255 (2019).</li><li>Seifert, L., Cordier, R., Orth, D., Courtine, Y., Croft, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+route+previewing+strategies+on+climbing+fluency+and+exploratory+movements.">Role of route previewing strategies on climbing fluency and exploratory movements.</a> PLoS One. 12 (4), 1-22 (2017).</li><li>Takeuchi, H., Habuchi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+method+for+analyzing+scan+path+data+obtained+by+eye+tracker.">A quantitative method for analyzing scan path data obtained by eye tracker.</a> IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007. , 283-286 (2007).</li><li>Takeuchi, H., Matsuda, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scan-path+analysis+by+the+string-edit+method+considering+fixation+duration.">Scan-path analysis by the string-edit method considering fixation duration.</a> 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012. , 1724-1728 (2012).</li><li>S&#225;iz, M. C., Marticorena, R., Anaiz, &. #. 1. 9. 3. ;., Zapara&#237;n, M. J., Peralbo, M., Risso, A., Barca, A., Duarte, B., Almeda, L., Brenlla, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Task+analysis+with+eye+tracking+technology.+srl+at+Smartart.">Task analysis with eye tracking technology. srl at Smartart.</a> II Congress of the International Scientific Association of Psychopedagogy Proceedings. , 4093-4104 (2019).</li><li>Khedher, A. B., Jraidi, I., Frasson, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+Students'+Analytical+Reasoning+Using+Visual+Scan+Paths.">Tracking Students' Analytical Reasoning Using Visual Scan Paths.</a> IEEE 17th International Conference on Advanced Learning Technologies (ICALT). , 53-54 (2017).</li><li>Xia, C., Han, J., Qi, F., Shi, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+Human+Saccadic+Scanpaths+Based+on+Iterative+Representation+Learning.">Predicting Human Saccadic Scanpaths Based on Iterative Representation Learning.</a> IEEE Trans Image Process. 8 (7), 3502-3515 (2019).</li><li>Cerezo, R., Fern&#225;ndez, E., G&#243;mez, C., S&#225;nchez-Santill&#225;n, M., Taub, M., Azevedo, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multimodal+Protocol+for+Assessing+Metacognition+and+Self-Regulation+in+Adults+with+Learning+Difficulties.">Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties.</a> Journal of Visualized Experiments. , e60331 (2020).</li><li>Scherer, R., Siddiq, F., Tondeur, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+technology+acceptance+model+(TAM):+A+meta-analytic+structural+equation+modeling+approach+to+explaining+teachers'+adoption+of+digital+technology+in+education.">The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education.</a> Computers &amp; Education. 128 (0317), 13-35 (2019).</li><li>Bruder, C., Ei&#223;feldt, H., Maschke, P., Hasse, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differences+in+monitoring+between+experts+and+novices.">Differences in monitoring between experts and novices.</a> Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 57 (1), 295-298 (2013).</li><li>Azevedo, R., Ga&#353;evi&#263;, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+Multimodal+Multichannel+Data+about+Self-Regulated+Learning+with+Advanced+Learning+Technologies:+Issues+and+Challenges.">Analyzing Multimodal Multichannel Data about Self-Regulated Learning with Advanced Learning Technologies: Issues and Challenges.</a> Computers in Human Behavior. 96, 207-210 (2019).</li><li>Taub, M., Azevedo, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+Does+Prior+Knowledge+Influence+Eye+Fixations+and+Sequences+of+Cognitive+and+Metacognitive+SRL+Processes+during+Learning+with+an+Intelligent+Tutoring+System.">How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System.</a> International Journal of Artificial Intelligence in Education. 29 (1), 1-28 (2019).</li><li>IBM Corp. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SPSS Statistical Package for the Social Sciences (SPSS) (Version 24). , (2016).</li><li>. Orange Software Package Available from: <a target="_blank" href="https://orange.biolab.si/docs/">https://orange.biolab.si/docs/</a> (2020)</li></ol>

The research results indicated that the average fixation duration on the relevant stimuli was longer among participants with previous knowledge. Likewise, the focus of attention on this group is on the middle points of information (proximal and distal)7. The results of this study have revealed differences in the way participants processed the information. Furthermore, their processing was not always linked to the initial grouping (University of Experience Students, University Teachers and Graduate...

Eye-tracking methodology applied to behavioral analysis in learning 
Eye-tracking methodology, among other functional uses, is applied to the study of human behavior, specifically during task resolution. This technique facilitates monitoring and analysis during the completion of learning tasks1. Specifically, the attention levels of students at different points of the learning process (start, development, and end) in different subjects (History, Mathematics, Science, etc.) can be studied with the use of eye-tracking technology. In addition, if the task includes the use of videos with a voice that guides the learning process, Self-Regulated Learning (SRL) is facilitated. Therefore, the implementation of eye-tracking technology in the analysis of tasks to which SRL (that include the use of videos) is proposed as a significant resource to understand how learning is developed2,3,4. This combination will also mean that the differences between instructional methods (with or without SRL, etc.) may be checked with different types of students (with or without prior knowledge, etc.)5. In contrast, the presentation of multi-channel information (simultaneous presentation of both auditory and visual information, whether verbal, written, or pictorial) can facilitate both the recording and the analysis of relevant versus non-relevant information from the above-mentioned variables6. Students with prior knowledge exposed to multimedia learning channels appear to learn more effectively than those with little or no prior knowledge. Students with high levels of prior knowledge of the subject matter will integrate textual and graphical information more effectively7. This functionality has been observed in the learning of texts8 that include images9. Eye-tracking technology offers information on where attention is focused and for how long. These data give insight into the development of a learning process in a more precise way than through the simple observation of the resolution process during the completion of a task. Also, the analysis of these indicators facilitates the study of whether the student develops deep or superficial learning. Furthermore, the relationship between these data and the learning results facilitates the validation of the information obtained with eye-tracking technology4,10. In fact, this technique together with SRL are increasingly used in Higher Education and in Adult Education11 learning environments, both on regulated and on non-regulated courses12.
Eye-tracking technology offers different metrics: distance, speed, acceleration, density, dispersion, angular velocity, transitions between Areas of Interest (AOI), sequential order of AOI, visits in the fixations, saccades, scan path and heat map parameters. However, the interpretation of these data is complex and requires the use of supervised (regression, decision trees, etc.) and unsupervised (k-means cluster techniques, etc.)13,14 data-mining techniques. These metrics can be applied for monitoring the behavior of the same subject over time or for a comparison between several subjects and their performance with the same task15, by analyzing the difference between participants with previous knowledge versus no previous knowledge16. Recent research11,17 has revealed that novice apprentices fixate longer on the stimuli (i.e., there is a greater fixation frequency while similar scan-path patterns are recorded). The average duration of fixation was longer for experts than for novices. The experts presented their focus of attention on the middle points of the information (proximal and central), differences that may also be seen in the visualization points within the AOI on the heat maps.
Interpretation of metrics in eye tracking 
Recent studies18 have indicated that information acquisition is related to the number of ocular fixations on the stimuli. Another important metric is the saccade, which is defined as the rapid and sudden movement of a fixation with an interval of [10 ms, 100 ms]. Sharafi et al. (2015)18 found differences in the number of saccades, depending on the information coding phase of the student. Another relevant parameter is the scan-path, a metric that captures the chronological order of the steps that the participant performs for the resolution of the learning task within the AOI defined by the researcher18. Similarly, eye-tracking technology can be used to predict the participant&#39;s level of understanding, which appears to be related to the number of fixations. Recent studies have indicated that variability in gaze behavior is determined by the properties of the image (position, intensity, color, and orientation), the instructions for performing the task, and the type of information processing (learning style) of the participant. These differences are detected by analyzing the student&#39;s interaction with the different AOI19. Quantitative20 (frequency analysis) and/or qualitative or dynamic21 (scan path) techniques can be used to analyze the data collected from the different metrics. The former techniques are analyzed with traditional statistical techniques (frequency analysis, mean difference, variance difference, etc.) and the latter are analyzed with Machine Learning techniques (Euclidean distances with string-edit methods21,22, and clustering17). The application of these techniques facilitates clustering, by considering different characteristics of the subjects. One study17 found that the more expert the student, the more effective the spatial and temporal information processing strategy that is implemented. A descriptive table of the measurement parameters that were used in this study can be consulted below in Table 1.
Table 1: Most representative parameters that can be obtained with the eye-tracking technique, adapted from S&#225;iz, Zapara&#237;n, Marticorena, and Velasco (2019).20&#160;<a href="https://www.jove.com/files/ftp_upload/62103/Copia de JoVE_Table_1.xls" target="_blank">Please click here to download this Table.</a>
Application of the eye-tracking methodology to the study of the learning process 
The use of the technological advances and the data-analysis techniques described above5 will add greater precision to behavioral analysis of learners during problem solving in the different phases of information processing (task initiation, information processing, and task resolution). It will all facilitate individual behavioral analysis, which will in turn permit the grouping of students with similar characteristics24. Likewise, predictive techniques (decision trees, regression techniques, etc.)25 can be applied to learning, related both to the number of fixations and to the task-resolution results of each student. This functionality is a very important advance in the knowledge of how each student learns and for the proposal of personalized learning programs within different groups (people with or without learning difficulties26). Therefore, the use of this technique will contribute towards the achievement of personalization and optimization of learning27. Life-long learning must be understood as a cycle of continuous improvement since the knowledge of society is constantly advancing and progressing. Evolutionary psychology indicates that resolution skills and effectiveness in information processing decrease with age. Specifically, saccade frequency, amplitude, and speed of eye movements among adults have been found to decrease with age. In addition, at older ages, attention is focused on the lower areas of visual scenes, which is related to deficits in working memory14. Nevertheless, activation increases in the frontal and prefrontal areas at an older age, which appears to compensate for these deficits in task resolution. This aspect includes the level of previous knowledge and the cognitive compensation strategies that the subject can apply. Experienced participants learn more efficiently, since they manage attention more effectively, due to the application of automated supervision processes28. In addition, if the information to be learned is imparted through SRL techniques, the aforementioned deficiencies are mitigated17. The use of such techniques means that visual tracking patterns are very similar, both in subjects without prior knowledge and in subjects with prior knowledge7.
In summary, the analysis of multimodal-multichannel data on SRL obtained with the use of advanced learning (eye-tracking) technologies is key to understanding the interaction between cognitive, metacognitive, and motivational processes, and their impact on learning29. The results and the study of differences in learning have implications for the design of learning materials and intelligent tutoring systems, both of which will enable personalized learning that is likely to be more effective and satisfactory for the student30.
In this research, there were two investigation questions asked: (1) Will there be significant differences in the learning results and in the ocular fixation parameters between students and expert versus non-expert teachers in Art History differentiating students with official degrees versus students with non-official degrees (University of Experience - Adult education)? and (2) Will clusters of each participant with learning results and ocular fixation parameters coincide with the type of participants (students with official degrees, students with non-official degrees (University of Experience - Adult education) and teachers)?

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>iViewer XTM</td>
			<td>iViewer</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SMI Experimenter Center 3.0</td>
			<td>SMI</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SMI Be Gaze</td>
			<td>SMI</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
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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.

The 36 participants recruited for the present study were from three groups of adults (students from the university of experience, university professors, and undergraduate and master&#39;s degree students) with ages ranging between [18&#160;and 69] years (Table 2). The protocol was tested over 20 months at the University of Burgos. An outline of the development can be seen in Table 4.
Tab...

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

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 ...

We present a protocol for the behavioral analysis of 15 to 70 year old adults engaged in different processes. Undertaking tasks assigned for self-regulated learning. The participants, university teachers and students, and others from the University of Experience, were monitored with eye tracking devices and the data were analyzed with data mining techniques.This protocol was performed in compliance with the procedural regulations of the bioethical committee of the University of Burgos, Spain. In this research, there were two investigation questions ask. Research question one, will there be significant differences in the learning results and in the ocular fixation parameters between students and expert versus non-expert teachers in art history, differentiating students with official degrees versus students with non-official degrees, University of Experience adult education?And research question two:Will clusters of each participant with learning results and ocular fixation parameters coincide with the type of participants, students with official degrees, students with non-official degrees, University of Experience adult education, and teachers? Participant recruitment. Recruit participants from among a group of adults within two environments, students and teacher, with an age rank of 18 to 70 years old in the environment of higher education, formal and non-formal education.Include participant with normal or corrected to normal vision and hearing. Exclude participant with neurological, psychiatric, and sleep disorders, disability related to educational special needs, perceptual difficulties, impaired sight and hearing, and cognitive disabilities. In this study, we worked in a definitive sample amounted to 39 participant, six student from the University of Experience.One participant was excluded in the category of student from the University of Experience because of visual difficulties. 25 university professors in the discipline of health science, engineering, and history and heritage, and nine undergraduate and master's students following courses in health science, engineering, and history and heritage. Data collection, obtain informed consent.Before the test, inform each participant of the aim of the study and the collection, treatment and storage of their data. The agreement of each participant is given by signing the informed consent form. Then, before starting the task, the interviewer, an expert in the field, passes a questionnaire about question on age, gender, occupation, and prior knowledge of the subject matter.In this case, the origin and historical development of monasteries in Europe. Experimental procedure.Calibration. Inform the participant about how eye tracking technology works, and how the information will be collected and recorded and calibrated.It is explained to the participant that a valid desk require proper positioning. To do so, the participant has to sit at a certain distance, 45 to 60 centimeters from the monitor. The distance will depend on the height of the participant, lower the height, the shorter the distance.The participant is informed that a series of points will appear on the cardinal point of the screen, and that each time a point appears, the participant must observe it with his eyes. Take the calibration setting. Performing the learning task.Explain the contents of the task to the participant. The learning task consists of the explanation of the origin of monasteries in Europe. The assignment is presented in a video voiceover using SRL methodology.The information is presented through auditory and visual channels, including written text and images. Watch the video clip. Performing the crossword puzzle on a Moodle based virtual platform.Data analysis. Choose the Area Of Interest, AOI. AOIs are defined in the video and are divided into AOIs that contain relevant information versus AOIs that include non-relevant information.Extract the database relating to the parameters for AOIs fixations. Import the database, including a statistical processing software package, and select the option Analyze, and then Classify, follow by the option K-Means Cluster. Then select cross table in the statistical software package, for example SPSS, followed by the ANOVA option, to analyze the differences between the participant.Perform a visualization analysis of the data. Extract the data of the parameters of detailed statistics and make the statistical analysis. Personalized learning proposals.Perform an intervention program to improve learning outcomes among the participants detected in the cluster analysis due to their lower scores. Representative results. 36 participants recruited for the present study were from three groups of adult students from the University of Experience, university professors, and undergraduate and master's degree students with age ranging between 22 and 69 years old.The protocol was tested over 20 months at the University of Burgos. The gaze position parameters of the fixation were analyzed. The group of men and women in each of the participating group, University of Experience student, university teacher, and graduate and masters students, each completed the task in different ways.No significant differences were found in any of the fixation parameters, but a trend towards differences for fixation average pupil size Y, fixation and average pupil diameter, and fixation dispersion X was noted, although with low effect values. Then the key means clusters was applied to study whether there were different groupings in the initial research group, with respect to the results in the parameters of fixation position, previous knowledge, and crossword puzzle results. Three clusters were found.Also a group table was prepared between the values of cluster of a group membership assigned to each participant with respect to the category type of participant, University of Experience student, university professors, and university students. Each shows the position of participants within the cluster with respect to the three groups for the fixation position parameters. Significant differences were found depending on the background knowledge, variable in Diversion Duration 1.Analyzing the input, duel, and output time for each stimulus inserted in each AOI. It can therefore be concluded that the way of entering, remaining, and exiting in the different AOIs was different depending on the variable participant group during the initial phase of information access. Differences were also found in the average fixation duration parameters.Longer fixations refer to the participants spending more time analyzing and interpreting the information content within the different AOIs. Bonferroni means different test was applied to establish group membership, with which it was established that they were between the group University of Experience students and the group of university professors. The means were higher from group one, University of Experience students, where participant spend more time analyzing and interpreting the AOIs during the data entry phase.Based on the result found in this study, the development of a personalized learning program was proposed to improve task resolution learning outcomes. In this study, the program will focus on the reinforcement of the concept worked on in the video, for which an extension and specification of the concepts will be made. The results obtained in this data analysis give information to the teacher to adjust the presentation of the task and even the characteristics of each learner or group of learners.Conclusions.The presentation of information in way, through they use information channel, auditory, visual, or both. With SRL activity, the methodology, together with the use of a tracking technology and machine learning technique. It is key to understanding how learning processes information, and to being enabled to offer a personalized learning design according to educational needs for each So promoting the development assesses for learning.

eye-tracking-technology-data-mining-techniques-used-for-behavioral

Research

JoVE Journal

Behavior

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Spatial cognition research in rodents typically employs the use of maze tasks, whose attributes vary from one maze to the next. These tasks vary by their behavioral flexibility and required memory duration, the number of goals and pathways, and also the overall task complexity. A confounding feature in many of these tasks is the lack of control over the strategy employed by the rodents to reach the goal, e.g., allocentric (declarative-like) or egocentric (procedural) based strategies. The double-H maze is a novel water-escape memory task that addresses this issue, by allowing the experimenter to direct the type of strategy learned during the training period. The double-H maze is a transparent device, which consists of a central alleyway with three arms protruding on both sides, along with an escape platform submerged at the extremity of one of these arms.
Rats can be trained using an allocentric strategy by alternating the start position in the maze in an unpredictable manner (see protocol 1; &#167;4.7), thus requiring them to learn the location of the platform based on the available allothetic cues. Alternatively, an egocentric learning strategy (protocol 2; &#167;4.8) can be employed by releasing the rats from the same position during each trial, until they learn the procedural pattern required to reach the goal. This task has been proven to allow for the formation of stable memory traces.
Memory can be probed following the training period in a misleading probe trial, in which the starting position for the rats alternates. Following an egocentric learning paradigm, rats typically resort to an allocentric-based strategy, but only when their initial view on the extra-maze cues differs markedly from their original position. This task is ideally suited to explore the effects of drugs/perturbations on allocentric/egocentric memory performance, as well as the interactions between these two memory systems.

The goal of this protocol is to investigate spatial cognition in rodents. The double-H water maze is a novel test, which is particularly useful to elucidate the different components of learning, consolidation and memory, as well as the interplay of memory systems.

Spatial cognition research in rodents typically employs the use of maze tasks, whose attributes vary from one maze to the next. These tasks vary by their ...

Combined Shuttle-Box Training with Electrophysiological Cortex Recording and Stimulation as a Tool to Study Perception and Learning

Shuttle-box avoidance learning is a well-established method in behavioral neuroscience and experimental setups were traditionally custom-made; the necessary equipment is now available by several commercial companies. This protocol provides a detailed description of a two-way shuttle-box avoidance learning paradigm in rodents (here Mongolian gerbils; Meriones unguiculatus) in combination with site-specific electrical intracortical microstimulation (ICMS) and simultaneous chronical electrophysiological in vivo recordings. The detailed protocol is applicable to study multiple aspects of learning behavior and perception in different rodent species.
Site-specific ICMS of auditory cortical circuits as conditioned stimuli here is used as a tool to test the perceptual relevance of specific afferent, efferent and intracortical connections. Distinct activation patterns can be evoked by using different stimulation electrode arrays for local, layer-dependent ICMS or distant ICMS sites. Utilizing behavioral signal detection analysis it can be determined which stimulation strategy is most effective for eliciting a behaviorally detectable and salient signal. Further, parallel multichannel-recordings using different electrode designs (surface electrodes, depth electrodes, etc.) allow for investigating neuronal observables over the time course of such learning processes. It will be discussed how changes of the behavioral design can increase the cognitive complexity (e.g. detection, discrimination, reversal learning).

Shuttle-box avoidance learning is well-established in behavioral neuroscience. This protocol describes how shuttle-box learning in rodents can be combined with site-specific electrical intracortical microstimulation (ICMS) and simultaneous chronical in vivo recordings as a tool to study multiple aspects of learning and perception.

Shuttle-box avoidance learning is a well-established method in behavioral neuroscience and experimental setups were traditionally custom-made; the ...

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 ...

Eye-Tracking Control to Assess Cognitive Functions in Patients with Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with pathological involvement of upper and lower motoneurons, subsequently leading to progressive loss of motor and speech abilities. In addition, cognitive functions are impaired in a subset of patients. To evaluate these potential deficits in severely physically impaired ALS patients, eye-tracking is a promising means to conduct cognitive tests. The present article focuses on how eye movements, an indirect means of communication for physically disabled patients, can be utilized to allow for detailed neuropsychological assessment. The requirements, in terms of oculomotor parameters that have to be met for sufficient eye-tracking in ALS patients are presented. The properties of stimuli, including type of neuropsychological tests and style of presentation, best suited to successfully assess cognitive functioning, are also described. Furthermore, recommendations regarding procedural requirements are provided. Overall, this methodology provides a reliable, easy to administer and fast approach for assessing cognitive deficits in patients who are unable to speak or write such as patients with severe ALS. The only confounding factor might be deficits in voluntary eye movement control in a subset of ALS patients.

Cognitive deficits are common in about one third of patients with amyotrophic lateral sclerosis, a neurological condition leading to progressive impairments in speech and movement abilities. To conduct cognitive tests in patients unable to speak or write a reliable and easy to administer eye-tracking paradigm was developed.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with pathological involvement of upper and lower motoneurons, subsequently leading to ...

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be time-intensive and ambiguous to quantify beyond simple success rates. Here, we describe the training procedure for reach-and-pull tasks with ETH Pattus, a robotic platform for automated forelimb reaching training that records pulling and hand rotation movements in rats. Kinematic quantification of the performed pulling attempts reveals the presence of distinct temporal profiles of movement parameters such as pulling velocity, spatial variability of the pulling trajectory, deviation from midline, as well as pulling success. We show how minor adjustments in the training paradigm result in alterations in these parameters, revealing their relation to task difficulty, general motor function or skilled task execution. Combined with electrophysiological, pharmacological and optogenetic techniques, this paradigm can be used to explore the mechanisms underlying motor learning and memory formation, as well as loss and recovery of function (e.g. after stroke).

A paradigm is presented for training and analysis of an automated skilled reaching task in rats. Analysis of pulling attempts reveals distinct subprocesses of motor learning.

Skilled reaching tasks are commonly used in studies of motor skill learning and motor function under healthy and pathological conditions, but can be ...

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing and the perception of the visual world. The recording of eye-gaze patterns has provided good evidence for the contribution of both the visual context and linguistic/world knowledge to language comprehension. Initial research assessed object-context effects to test&#160;theories of modularity in language processing. In the introduction, we describe how subsequent investigations have taken the role of the wider visual context in language processing as a research topic in its own right, asking questions such as how our visual perception of events and of speakers contributes to&#160;comprehension informed by&#160;comprehenders&#39; experience. Among the examined aspects of the visual context are actions, events, a speaker&#39;s gaze, and emotional facial expressions, as well as spatial object configurations. Following an overview of the eye-tracking method and its different applications, we list the key steps of the methodology in the protocol, illustrating how to successfully use it to study visually-situated language comprehension. A final section presents three sets of representative results and illustrates the benefits and limitations of eye tracking for investigating the interplay between the perception of the visual world and language comprehension.

The present article reviews an eye-tracking methodology for studies on language comprehension. To obtain reliable data, key steps of the protocol must be followed. Among these are the correct set-up of the eye tracker (e.g., ensuring good quality of the eye and head images) and accurate calibration.

The present work is a description and an assessment of a methodology designed to quantify different aspects of the interaction between language processing ...

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during active behavior. An important tool for quantifying behavior is eye tracking, which is an indispensable tool for studying visual attention. However, eye tracking is challenging to use concurrently with invasive electrophysiology and this approach has consequently been little used. Here, we present a proven experimental protocol to conduct single-neuron recordings with simultaneous eye tracking in humans. We describe how the systems are connected and the optimal settings to record neurons and eye movements. To illustrate the utility of this method, we summarize results that were made possible by this setup. This data shows how using eye tracking in a memory-guided visual search task allowed us to describe a new class of neurons called target neurons, whose response was reflective of top-down attention to the current search target. Lastly, we discuss the significance and solutions to potential problems of this setup. Together, our protocol and results suggest that single-neuron recordings with simultaneous eye tracking in humans are an effective method to study human brain function. It provides a key missing link between animal neurophysiology and human cognitive neuroscience.

We describe a method to conduct single-neuron recordings with simultaneous eye tracking in humans. We demonstrate the utility of this method and illustrate how we used this approach to obtain neurons in the human medial temporal lobe that encode targets of a visual search.

Intracranial recordings from patients with intractable epilepsy provide a unique opportunity to study the activity of individual human neurons during ...

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world around them. Eye tracking is a safe and non-invasive way to collect gaze data from infants, and the preferential looking paradigm is simple to design and only requires the infant to be attending to the screen. By simultaneously showing two visual stimuli that differ in one dimension, we can assess whether infants show different looking behavior for either stimulus, thus demonstrating sensitivity to that difference. The challenges in such experimental approaches are that experiments must be kept brief (no more than 10 min) and be carefully controlled such that the two stimuli differ in only one way. The interpretation of null results must also be carefully considered. In this paper, we illustrate a successful example of an infant eye tracking study with a preferential looking paradigm to discover that 6-month-olds are sensitive to linguistic cues in a signed language despite having no prior exposure to signed language, suggesting that infants possess intrinsic or innate sensitivities to these cues.

Eye tracking studies using a preferential looking paradigm can be used to study infants&#39;&#160;emerging understanding of, and attention to, their external visual world.

We discuss the use of the preferential looking paradigm in eye tracking studies in order to study how infants develop, understand, and attend to the world ...

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 ...