Abstract
The results of experimental testing of the developed software for matching the focus of the student’s gaze with the structure of the training content on a computer monitor are presented in this paper. The use of widespread equipment is assumed: a laptop with a built-in camera or one additional camera. Initial processing of the face image, selection of eye areas is carried out using the OpenCV library. An appropriate algorithm for calculating the center of the eye pupil and the point on the monitor corresponding to the current focus of the gaze has been developed. The influence of the system calibration process with different schemes of calibration point display, its delay time on the screen and location of the additional camera according to the accuracy of the calculation of the coordinates of the gaze focus is investigated. Based on the performed experiments, it was defined that the error of gaze focus recognition with using two cameras can be reduced to 4–10%. The experiment in order to improve the calibration processes and evaluate the capabilities of the developed software for use on a laptop with only one built-in camera involving a group of students was carried out. The proposed approach makes it possible for objective measurement of each student working time with one or another part of the content. The lecturer will have the opportunity to improve the content by highlighting significant parts that receive little attention and simplifying those elements that students process for an unreasonably big amount of time. It is planned to integrate the developed software into the LMS Moodle in the future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Young, L.R., Sheena, D.: Survey of eye movement recording methods. Behav. Res. Methods Instrum. 7, 397–429 (1975)
Wadehn, F., Weber, T., Mack, D.J., Heldt, T., Loeliger, H.-A.: Model-based separation, detection, and classification of eye movements. IEEE Trans. Biomed. Eng. 67, 588–600 (2019). https://doi.org/10.1109/TBME.2019.2918986
Understanding different aspects of learning. https://www.tobiipro.com/applications/scientific-research/education. Accessed 29 July 2022
Gwon, S.Y., Cho, C.W., Lee, H.C., Lee, W.O., Park, K.R.: Robust eye and pupil detection method for gaze tracking. Int. J. Adv. Rob. Syst. 10(98), 1–7 (2013). https://doi.org/10.5772/55520
Cho, C.W., et al.: Gaze detection by wearable eye-tracking and NIR LED-based head-tracking device based on SVR. ETRI J. 34, 542–552 (2012). https://doi.org/10.4218/etrij.12.0111.0193
Clay, V., König, P., Koenig, S.: Eye tracking in virtual reality. J. Eye Mov. Res. 12(1) (2019). https://doi.org/10.16910/jemr.12.1.3
Naqvi, RA., Arsalan, M., Batchuluun, G., Yoon, H.S., Park, K.R.: Deep learning-based gaze detection system for automobile drivers using a NIR camera sensor. Sensors 18(2), 456 1–34 (2018). https://doi.org/10.3390/s18020456
Krafka, K., et al.: Eye tracking for everyone. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2176–2184 (2016). https://doi.org/10.1109/CVPR.2016.239
Wang, Y., Wang, X., Wu, Y.: A simple model of reading eye movement based on deep learning. IEEE Access 8, 193757–193767 (2020). https://doi.org/10.1109/ACCESS.2020.3033382
Skodras, E., Kanas, V.G., Fakotakis, N.: On visual gaze tracking based on a single low cost camera. Signal Process.: Image Commun. 36, 29–42 (2015). https://doi.org/10.1016/j.image.2015.05.007
Ferhat, O., Vilariño, F.: Low cost eye tracking: the current panorama. Comput. Intell. Neurosci. 2016, 1–14 (2016). https://doi.org/10.1155/2016/8680541
Timm, F., Barth, E.: Accurate eye centre localisation by means of gradients. In: Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 125–130 (2011). https://doi.org/10.5220/0003326101250130
Wood, E., Bulling, A.: EyeTab: model-based gaze estimation on unmodified tablet computers. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 207–210 (2014). https://doi.org/10.1145/2578153.2578185
Bulatnikov, Y. V., Goeva, A. A.: Sravnenie bibliotek kompyuternogo zreniya dlya primeneniya v prilozhenii, ispolzuyushchem tekhnologiyu raspoznavaniya ploskikh izobrazheniy (comparison of computer vision libraries for use in an application using flat image recognition technology). Vestnik Moskovskogo gosudarstvennogo universiteta pechati, pp. 85–91 (2015)
Shakhin, G.: Sravnitelnyy analiz bibliotek kompyuternogo zreniya (comparative analysis of computer vision libraries). Colloquium-J. 24(48), 53–55 (2019). https://doi.org/10.24411/2520-6990-2019-10812
Ji, Y., Wang, S., Lu, Y., Wei, J., Zhao, Y.: Eye and mouth state detection algorithm based on contour feature extraction. J. Electron. Imag. 27(5), 051205 (2018). https://doi.org/10.1117/1.JEI.27.5.051205
Chandrappa, D., Akshay, G., Ravishankar, M.: Face detection using a boosted cascade of features using OpenCV. In: Venugopal, K.R., Patnaik, L.M. (eds.) ICIP 2012. CCIS, vol. 292, pp. 399–404. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31686-9_46
Facial point annotations. https://ibug.doc.ic.ac.uk/resources/facial-point-annotations. Accessed 29 July 2022
Shape predictor 68 face landmarks. https://github.com/davisking/dlib-models/blob/master/shape_predictor_68_face_landmarks.dat.bz2. Accessed 29 July 2022
Tracking your eyes with python. https://medium.com/@stepanfilonov/tracking-your-eyes-with-python-3952e66194a6. Accessed 29 July 2022
Obrobka rastrovykh zobrazhen (raster image processing). https://www.tobiipro.com/applications/scientific-research/education. Accessed 29 July 2022
Suzuki, S., et al.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30, 32–46 (1985)
Welzl, E.: Smallest enclosing disks (balls and ellipsoids). In: Maurer, H. (ed.) New Results and New Trends in Computer Science. LNCS, vol. 555, pp. 359–370. Springer, Heidelberg (1991). https://doi.org/10.1007/BFb0038202
Cech, J., Soukupova, T.: Real-time eye blink detection using facial landmarks. In: Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague, pp. 1–8 (2016)
Shynkarenko, V., Raznosilin, V., Snihur, Y.: Automated monitoring of content demand in distance learning. In: Proceedings of the 17th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, vol. I, pp. 158–172. CEUR Workshop Proceedings, vol. 3013 (2021). http://ceur-ws.org/Vol-3013/20210158.pdf
Shynkarenko, V., Zhevago, O.: Visualization of program development process. In: IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), vol. 2, pp. 142–145. IEEE (2019). https://doi.org/10.1109/STC-CSIT.2019.8929774
Shynkarenko, V., Zhevago, O.: Development of a toolkit for analyzing software debugging processes using the constructive approach. East.-Eur. J. Enterp. Technol. 5(2), 29–38 (2020). https://doi.org/10.15587/1729-4061.2020.215090
Shynkarenko, V., Zhevaho, O.: Constructive modeling of the software development process for modern code review. In: IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 392–395. IEEE (2020). https://doi.org/10.1109/CSIT49958.2020.9322002
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Shynkarenko, V., Raznosilin, V., Snihur, Y., Chyhir, R. (2022). Experimental Research of Educational Content Tracking by Students Group for Distance Learning. In: Ermolayev, V., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2021. Communications in Computer and Information Science, vol 1698. Springer, Cham. https://doi.org/10.1007/978-3-031-20834-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-20834-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20833-1
Online ISBN: 978-3-031-20834-8
eBook Packages: Computer ScienceComputer Science (R0)