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Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning

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Abstract

Multimedia is an essential and integral part of electronic learning (e-learning). In this study, teaching performance and student learning experience are measured using real-time multimedia processing tools and techniques for the e-learning paradigm. Visual attention and visual engagement analysis are performed using two developed algorithms. Video lectures are recorded and delivered to students in e-learning pedagogical setup, which are examined for the visual attention and visual engagement of the student and teacher, respectively. Proposed methodology integrates the assessment on both student and teacher ends. Multimedia processing of video lectures for teaching performance produces scoring dataset. The same methodology on student end for visual attention is used to investigate student experience. These types of datasets then reduced to time-based datasets from the image-based dataset. Correlation and association of both datasets provide the opportunity to relate both student experience and teaching performance as well as to move forward to create content that is more useful. Computational performance of the developed algorithms is compared using different video lectures with their processed frames per second, which is analyzed as per their corresponding bins. Mean, max, and median of the processed frames of all the processed videos are also compared.

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References

  1. Gómez-Aguilar, D.A., et al.: Tap into visual analysis of customization of grouping of activities in eLearning. Comput. Hum. Behav. 47, 60–67 (2015)

    Article  Google Scholar 

  2. Farhan, M.: A methodology to enrich student-teacher interaction in eLearning. In: The 30th ACM/SIGAPP Symposium on Applied Computing (2015)

  3. Zain-Ul-Abidin, et al.: Analysis of video lecture’s images for the compression formats support for the students in eLearning paradigm. Sci. Int. 27(2), 1171–1176 (2015)

    Google Scholar 

  4. Mörwald, T., et al.: Advances in real-time object tracking. J. Real Time Image Proc. 10(4), 683–697 (2015)

    Article  Google Scholar 

  5. Farhan, M., et al.: Automated reply to students’ queries in e-Learning environment using Web-BOT. In: Artificial Intelligence (MICAI), 2012 11th Mexican International Conference on. IEEE (2012)

  6. Wang, Y., et al.: Image acquisition for digital camera based on emgu CV. Dianzi Keji Electron. Sci. Technol. 25(4), 31–32 (2012)

    Google Scholar 

  7. Ponomarenko, N., et al.: Analysis of HVS-metrics’ properties using color image database TID2013. In: International Conference on Advanced Concepts for Intelligent Vision Systems. Springer (2015)

  8. Khan, F.M.A., Masood, M.: The effectiveness of an interactive multimedia courseware with cooperative mastery approach in enhancing higher order thinking skills in learning cellular respiration. Procedia Soc. Behav. Sci. 176, 977–984 (2015)

    Article  Google Scholar 

  9. Kriegeskorte, N.: Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1, 417–446 (2015)

    Article  Google Scholar 

  10. Henriksen, S., Read, J.C.: Visual perception: a novel difference channel in binocular vision. Curr. Biol. 26(12), R500–R503 (2016)

    Article  Google Scholar 

  11. Liu, J., et al.: Human visual system consistent quality assessment for remote sensing image fusion. ISPRS J. Photogramm. Remote Sens. 105, 79–90 (2015)

    Article  Google Scholar 

  12. Rogowitz, B.E., Pappas T.N., de Ridder H.: Human vision and electronic imaging. Hum. Vis. Electron. Imaging 9394, 586 (2015)

    Google Scholar 

  13. Dickinson, S., Pizlo, Z.: Shape perception in human and computer vision. Springer (2015)

  14. Zarándy, Á., et al.: A real-time multi-camera vision system for UAV collision warning and navigation. J. Real Time Image Process. 1–16 (2014)

  15. Muhammad, M.I., et al.: Automated Web-bot implementation using machine learning techniques in eLearning paradigm. J. Appl. Environ. Biol. Sci. 4(7S), 90–98 (2014)

    Google Scholar 

  16. Fain, E., Smith, J.: Video lecture capture pedagogy context: does it matter and does it deliver?. In: Society for Information Technology and Teacher Education International Conference (2014)

  17. Aydin, M.K., Gürol, M., Vanderlinde, R.: Evaluating ICT integration in Turkish K-12 schools through teachers’ views. Eurasia J. Math. Sci. Technol. Educ. 12(4), 747–766 (2016)

    Google Scholar 

  18. Seguin, R.A., Morgan, E.H., Connor, L.M., Garner, J.A., King, A.C., Sheats, J.L., et al.: Rural food and physical activity assessment using an electronic tablet-based application, New York, 2013–2014. Prev Chronic Dis. 12, 150147 (2015). doi:10.5888/pcd12.150147

    Article  Google Scholar 

  19. Kizilcec, R.F., Papadopoulos, K., Sritanyaratana, L.: Showing face in video instruction: effects on information retention, visual attention, and affect. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014. ACM

  20. Wasserman, S., et al.: Assessing attention: relationship between circular visual-tracking and spatial span, and implication for electronic assessment. Arch. Phys. Med. Rehabil. 96(12), e15 (2015)

    Article  Google Scholar 

  21. Selvakumar, K., et al.: Real-time vision based driver drowsiness detection using partial least squares analysis. J. Signal Process. Syst. 1–12 (2015)

  22. Plyer, A., Le Besnerais, G., Champagnat, F.: Massively parallel Lucas Kanade optical flow for real-time video processing applications. J. Real Time Image Process. 11(4), 713–730 (2016)

    Article  Google Scholar 

  23. Saponara, S., et al.: Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing. J. Real Time Image Process. 1(4), 267–283 (2007)

    Article  Google Scholar 

  24. Sánchez, S., et al.: Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs. J. Real Time Image Process. 10(3), 469–483 (2015)

    Article  Google Scholar 

  25. Gomes, S.L., Rebouças, E.D.S., Neto, E.C., Papa, J.P., de Albuquerque, V.H., Rebouças Filho, P.P., Tavares, J.M.R.: Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput. Appl. 1–12 (2016)

  26. Salah, A.A., Alpaydin, E., Akarun, L.: A selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 420–425 (2002)

    Article  Google Scholar 

  27. Li, H., Ngan, K.N.: Saliency model-based face segmentation and tracking in head-and-shoulder video sequences. J. Vis. Commun. Image Represent. 19(5), 320–333 (2008)

    Article  Google Scholar 

  28. Kienzle, W., et al.: Center-surround patterns emerge as optimal predictors for human saccade targets. J. vis. 9(5), 7 (2009)

    Article  Google Scholar 

  29. Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an “early warning system” for educators: a proof of concept. Comput. Educ. 54(2), 588–599 (2010)

    Article  Google Scholar 

  30. Mathe, S., Sminchisescu, C.: Dynamic eye movement datasets and learnt saliency models for visual action recognition. In: Computer Vision–ECCV 2012, pp. 842–856. Springer (2012)

  31. Han, J.H., Finkelstein, A.: Understanding the effects of professors’ pedagogical development with Clicker Assessment and Feedback technologies and the impact on students’ engagement and learning in higher education. Comput. Educ. 65, 64–76 (2013)

    Article  Google Scholar 

  32. Thoms, B., Eryilmaz, E.: How media choice affects learner interactions in distance learning classes. Comput. Educ. 75, 112–126 (2014)

    Article  Google Scholar 

  33. Ba, J., Mnih, V., Kavukcuoglu, K.: Multiple object recognition with visual attention. arXiv preprint: arXiv:1412.7755, 2014

  34. Gittins, H., et al.: Multilevel engagement: theory and practice integration. In: Green Innovation and Future Technology: Engaging Regional SMEs in the Green Economy, pp. 41–56. Springer (2015)

  35. Yamaguchi, Y.: Image synthesis in consideration of a human visual system. In: The Visual Language of Technique, pp. 65–79. Springer (2015)

  36. Li, M.S., et al.: Performance improvement of multi-view video coding based on geometric prediction and human visual system. Int. J. Imaging Syst. Technol. 25(1), 41–49 (2015)

    Article  MathSciNet  Google Scholar 

  37. Iqbal, M.M., Farhan, M., Saleem, Y.: Personalization with agent in eLearning framework. Sci. Int. 27(1), 2541–2543 (2014)

    Google Scholar 

  38. Liu, K.-Y., et al.: A new parallel particle filter face tracking method based on heterogeneous system. J. Real Time Image Process. 7(3), 153–163 (2012)

    Article  Google Scholar 

  39. Thorpe, S., Fize, D., Marlot, C.: Speed of processing in the human visual system. Nature 381(6582), 520–522 (1996)

    Article  Google Scholar 

  40. Han, J., et al.: An infrared small target detecting algorithm based on human visual system. IEEE Geosci. Remote Sens. Lett. 13(3), 452–456 (2016)

    Google Scholar 

  41. Park, M.-C., Mun, S.: Overview of measurement methods for factors affecting the human visual system in 3d displays. J. Disp. Technol. 11(11), 877–888 (2015)

    Article  Google Scholar 

  42. Ahmad, J., et al.: Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems. J. Real Time Image Process. 1–17 (2015). doi:10.1007/s11554-015-0536-0

  43. Chen, B.-W., et al.: QoE-enabled big video streaming for large-scale heterogeneous clients and networks in smart cities. IEEE Access 4, 97–107 (2016)

    Article  Google Scholar 

  44. Meng, H., et al.: Real-time human action recognition on an embedded, reconfigurable video processing architecture. J. Real Time Image Process. 3(3), 163–176 (2008)

    Article  Google Scholar 

  45. Kapela, R., McGuinness, K., O’Connor, N.E.: Real-time field sports scene classification using colour and frequency space decompositions. J. Real Time Image Process. 1–13 (2014)

  46. Paul, A., et al.: Video search and indexing with reinforcement agent for interactive multimedia services. ACM Trans. Embed. Comput. Syst. (TECS) 12(2), 25 (2013)

    Google Scholar 

  47. Razzaq Malik, K., et al.: Data security and privacy in cloud computing: threat level indications. Sci. Int. 26(5), 1991–1996 (2014)

    Google Scholar 

  48. Jabbar, S., et al.: Trust model at service layer of cloud computing for educational institutes. J. Supercomput. 72(1), 58–83 (2016)

    Article  Google Scholar 

  49. Naseer, M.K., Jabbar, S., Zafar, I.: A novel trust model for selection of Cloud Service Provider. In: Computer Applications and Research (WSCAR), 2014 World Symposium on. (2014)

  50. Malik, K.R., Ahmad, T., Farhan, M., Aslam, M., Jabbar, S., Khalid, S., Kim, M.: Big-data: transformation from heterogeneous data to semantically-enriched simplified data. Multimed. Tools Appl. 75(20), 12727–12747 (2015). doi:10.1007/s11042-015-2918-5

    Article  Google Scholar 

  51. Khalid, S., et al.: Accurate and efficient shape matching approach using vocabularies of multi-feature space representations. J. Real Time Image Process. 1–17 (2015). doi:10.1007/s11554-015-0545-z

  52. Chen, C., Liu, K., Kehtarnavaz, N.: Real-time human action recognition based on depth motion maps. J. Real Time Image Process. 12(1), 155–163 (2016)

    Article  Google Scholar 

  53. Varfolomieiev, A., Lysenko, O.: An improved algorithm of median flow for visual object tracking and its implementation on ARM platform. J. Real Time Image Process. 11(3), 527–534 (2016)

    Article  Google Scholar 

  54. Joseph Raj, A.N., Staunton, R.C.: Video-rate calculation of depth from defocus on a FPGA. J. Real Time Image Process. 1–12 (2014). doi:10.1007/s11554-014-0480-4

  55. Rautaray, S.S., Agrawal, A.: Vision based hand gesture recognition for human computer interaction: a survey. Artif. Intell. Rev. 43(1), 1–54 (2015)

    Article  Google Scholar 

  56. Farhan, M., et al.: Extracting parameters from e-feedback text data based on keyword based parsing in elearning environment. Sci. Int. 26(3), 1395–1402 (2014)

    Google Scholar 

  57. Poiesi, F., Cavallaro, A.: Predicting and recognizing human interactions in public spaces. J. Real Time Image Process. 10(4), 785–803 (2015)

    Article  Google Scholar 

  58. Ohlsson, N., Ståhl, M.: Model-Based Approach to Computer Vision and Automatic Control using Matlab Simulink for an Autonomous Indoor Multirotor System. Chalmers University of Technology, Gothenburg (2013)

    Google Scholar 

  59. Kaehler, A., Bradski, G.: Learning OpenCV. O’Reilly Media, Inc, Sebastopol (2014)

    Google Scholar 

  60. Anders, J., et al.: A hardware/software prototyping system for driving assistance investigations. J. Real Time Image Process. 11(3), 559–569 (2016)

    Article  Google Scholar 

  61. Li, C., et al.: Design and evaluation of a parallel and optimized light–tissue interaction-based method for fast skin lesion assessment. J. Real Time Image Process., 1–14 (2015). doi:10.1007/s11554-015-0494-6

  62. Shi, S.: Emgu CV Essentials. Packt Publishing Ltd, Birmingham (2013)

    Google Scholar 

  63. Razzaq Malik, K., et al.: Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data. Int. J. Distrib. Sens. Netw. 12(6), 9103265 (2016). doi:10.1155/2016/9103265

  64. Chambers, O., Milenkovic, J., Tasic, J.F.: A pre-processing scheme for real-time registration of dynamic contrast-enhanced magnetic resonance images. J. Real Time Image Process. 1–10 (2014). doi:10.1007/s11554-014-0468-0

  65. Cano, A., et al.: Parallelization strategies for markerless human motion capture. J. Real Time Image Process. 1–15 (2014). doi:10.1007/s11554-014-0467-1

  66. Chorianopoulos, K., Giannakos, M.N.: Usability design for video lectures. In: Proceedings of the 11th European Conference on Interactive TV and Video. 2013. ACM

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan

    Muhammad Farhan & Muhammad Aslam

  2. Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan

    Muhammad Farhan & Sohail Jabbar

  3. Network Lab, School of Computer Science and EngineeringKyungpook National University, Daegu, South Korea

    Sohail Jabbar

  4. Department of Computer Engineering, Bahria University, Islamabad, Pakistan

    Shehzad Khalid

  5. Division of Media Software, Sungkyul University, Anyang, 430742, Korea

    Mucheol Kim

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  1. Muhammad Farhan
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  2. Muhammad Aslam
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  3. Sohail Jabbar
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  4. Shehzad Khalid
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  5. Mucheol Kim
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Correspondence to Mucheol Kim.

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Farhan, M., Aslam, M., Jabbar, S. et al. Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning. J Real-Time Image Proc 13, 491–504 (2017). https://doi.org/10.1007/s11554-016-0662-3

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