Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series
Contemporary digital platforms provide a large number of web services for learning and professional growth. In most cases, educational web services only control access when connecting to resources and platforms. However, for educational and similar resources (internet surveys, online research), whic...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/13/2/141 |
| _version_ | 1827757737539272704 |
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| author | Evgeny Nikulchev Alexander Gusev Nurziya Gazanova Shamil Magomedov Anna Alexeenko Artem Malykh Pavel Kolyasnikov Sergey Malykh |
| author_facet | Evgeny Nikulchev Alexander Gusev Nurziya Gazanova Shamil Magomedov Anna Alexeenko Artem Malykh Pavel Kolyasnikov Sergey Malykh |
| author_sort | Evgeny Nikulchev |
| collection | DOAJ |
| description | Contemporary digital platforms provide a large number of web services for learning and professional growth. In most cases, educational web services only control access when connecting to resources and platforms. However, for educational and similar resources (internet surveys, online research), which are characterized by interactive interaction with the platform, it is important to assess user engagement in the learning process. A fairly large body of research is devoted to assessing learner engagement based on automatic, semi-automatic, and manual methods. Those methods include self-observation, observation checklists, engagement tracing based on learner reaction time and accuracy, computer vision methods (analysis of facial expressions, gestures, and postures, eye movements), methods for analyzing body sensor data, etc. Computer vision and body sensor methods for assessing engagement give a more complete objective picture of the learner’s state for further analysis in comparison with the methods of engagement tracing based on learner’s reaction time, however, they require the presence of appropriate sensors, which may often not be applicable in a particular context. Sensory observation is explicit to the learner and is an additional stressor, such as knowing the learner is being captured by the webcam while solving a problem. Thus, the further development of the hidden engagement assessment methods is relevant, while new computationally efficient techniques of converting the initial signal about the learner’s reaction time to assess engagement can be applied. On the basis of the hypothesis about the randomness of the dynamics of the time series, the largest Lyapunov exponent can be calculated for the time series formed from the reaction time of learners during prolonged work with web interfaces to assess the learner’s engagement. A feature of the proposed engagement assessment method is the relatively high computational efficiency, absence of high traffic loads in comparison with computer vision as well as secrecy from the learner coupled with no processing of learner’s personal or physical data except the reaction time to questions displayed on the screen. The results of experimental studies on a large amount of data are presented, demonstrating the applicability of the selected technique for learner’s engagement assessment. |
| first_indexed | 2024-03-11T08:54:37Z |
| format | Article |
| id | doaj.art-13fbefdab2284735bbc2023de3d9b3fa |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-11T08:54:37Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-13fbefdab2284735bbc2023de3d9b3fa2023-11-16T20:08:30ZengMDPI AGEducation Sciences2227-71022023-01-0113214110.3390/educsci13020141Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time SeriesEvgeny Nikulchev0Alexander Gusev1Nurziya Gazanova2Shamil Magomedov3Anna Alexeenko4Artem Malykh5Pavel Kolyasnikov6Sergey Malykh7Department of Digital Data Processing Technologies, MIREA—Russian Technological University, Moscow 119454, RussiaCenter of Advanced Technologies and Nanomaterials, Kuban State Technological University, Krasnodar 350072, RussiaDepartment of the Intelligent Cyber-Security System Department, MIREA—Russian Technological University, Moscow 119454, RussiaDepartment of the Intelligent Cyber-Security System Department, MIREA—Russian Technological University, Moscow 119454, RussiaDepartment of Applied Information Technologies, MIREA—Russian Technological University, Moscow 119454, RussiaCenter of Population Research, Ural Institute of Humanities, Ural Federal University, Ekaterinburg 620002, RussiaCenter of Population Research, Ural Institute of Humanities, Ural Federal University, Ekaterinburg 620002, RussiaDevelopmental Behavioral Genetics Lab, Psychological Institute of Russian Academy of Education, Moscow 125009, RussiaContemporary digital platforms provide a large number of web services for learning and professional growth. In most cases, educational web services only control access when connecting to resources and platforms. However, for educational and similar resources (internet surveys, online research), which are characterized by interactive interaction with the platform, it is important to assess user engagement in the learning process. A fairly large body of research is devoted to assessing learner engagement based on automatic, semi-automatic, and manual methods. Those methods include self-observation, observation checklists, engagement tracing based on learner reaction time and accuracy, computer vision methods (analysis of facial expressions, gestures, and postures, eye movements), methods for analyzing body sensor data, etc. Computer vision and body sensor methods for assessing engagement give a more complete objective picture of the learner’s state for further analysis in comparison with the methods of engagement tracing based on learner’s reaction time, however, they require the presence of appropriate sensors, which may often not be applicable in a particular context. Sensory observation is explicit to the learner and is an additional stressor, such as knowing the learner is being captured by the webcam while solving a problem. Thus, the further development of the hidden engagement assessment methods is relevant, while new computationally efficient techniques of converting the initial signal about the learner’s reaction time to assess engagement can be applied. On the basis of the hypothesis about the randomness of the dynamics of the time series, the largest Lyapunov exponent can be calculated for the time series formed from the reaction time of learners during prolonged work with web interfaces to assess the learner’s engagement. A feature of the proposed engagement assessment method is the relatively high computational efficiency, absence of high traffic loads in comparison with computer vision as well as secrecy from the learner coupled with no processing of learner’s personal or physical data except the reaction time to questions displayed on the screen. The results of experimental studies on a large amount of data are presented, demonstrating the applicability of the selected technique for learner’s engagement assessment.https://www.mdpi.com/2227-7102/13/2/141engagement assessmentlargest Lyapunov exponentreaction timeclickerweb serviceinvolvement |
| spellingShingle | Evgeny Nikulchev Alexander Gusev Nurziya Gazanova Shamil Magomedov Anna Alexeenko Artem Malykh Pavel Kolyasnikov Sergey Malykh Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series Education Sciences engagement assessment largest Lyapunov exponent reaction time clicker web service involvement |
| title | Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series |
| title_full | Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series |
| title_fullStr | Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series |
| title_full_unstemmed | Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series |
| title_short | Engagement Assessment for the Educational Web-Service Based on Largest Lyapunov Exponent Calculation for User Reaction Time Series |
| title_sort | engagement assessment for the educational web service based on largest lyapunov exponent calculation for user reaction time series |
| topic | engagement assessment largest Lyapunov exponent reaction time clicker web service involvement |
| url | https://www.mdpi.com/2227-7102/13/2/141 |
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