Physiological Measurement on Students’ Engagement in a Distributed Learning Environment

Chen Wang, Pablo Cesar

2015

Abstract

Measuring students’ engagement in a distributed learning environment is a challenge. In particular, a teacher gives a lecture at one location, while at the same time the remote students watch the lecture through a display screen. In such situation, it is difficult for the teacher to know the reaction at the remote location. In this paper, we conducted a field study to measure students’ engagement by using galvanic skin response (GSR) sensors, where students simultaneously watched the lecture at the two locations. Our results showed the students’ GSR response was aligned with the surveys, which means that during a distributed learning environment, GSR sensors can be used as an indicator on students’ engagement. Furthermore, our user studies resulted in non-engaging student learning experiences that would be difficult obtained at a lab condition. Based on the findings, we found that the patterns of GSR readings were rather different when compared to the previous relevant studies, where users were engaged. In addition, we noticed that the density of GSR response at the remote location was higher when compared to the one at the lecture room. We believe that our studies are beneficial on physiological computing, as we first presented the patterns of GSR sensors on non-engaging user experiences. Moreover, as an alternative method, GSR sensors can be easily implemented in a distributed learning environment to provide feedback to teachers.

References

  1. Daniel D. Garcia and Luke Segars. 2012. Technology that educators of computing hail (TECH): come, share your favorites!. In Proceedings of the 43rd ACM technical symposium on Computer Science.
  2. Education (SIGCSE 7812). ACM, New York, NY, USA, 682-682. DOI=10.1145/2157136.2157438.
  3. Erkollar, Alptekin, and B. J. Oberer. "Putting Google+ to the Test: Assessing Outcomes for Student Collaboration, Engagement and Success in Higher Education." Procedia-Social and Behavioral Sciences 83 (2013): 185-189.
  4. Strudler, Neal, and Karen Grove. "I see you: Using the affordances of Google+ to increase social and teaching presence in an online undergraduate teacher education course ISTE 2013." Cynthia Clark doctoral student, San Antonio, TX. Retrieved from http://www. isteconferenceorg/uploads/ISTE2013/HANDOUTS/ KEY_80520342/I STE2013ISeeYou_RP. pdf. Accessed on June 17 (2013): 2013.
  5. Chen Wang, Erik N. Geelhoed, Phil P. Stenton, and Pablo Cesar. 2014. Sensing a live audience. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems (CHI 7814). ACM, New York, NY, USA, 1909-1912.
  6. DOI=10.1145/2556288.2557154.
  7. Young, Forrest W., and Robert M. Hamer (ed.)(1987), Multidimensional Scaling: History,Theory, and Applications, Hillsdale, NJ: Erlbaum.
  8. Schiffman, Susan S., M. Lance Reynolds, and Forrest W. Young (1981), Introduction to Multidimensional Scaling: Theory, Methods, and Applications, NY: Academic Press.
  9. Trevor F. Cox and M.A.A. Cox (2000). Multidimensional Scaling, Second Edition. ISBN 1-58488-094-5.
  10. Ingwer Borg and Patrick J.F. Groenen. Modern Multidimensional Scaling: Theory and Applications. 2005 Springer Science+Buiness Media, Inc. ISBN-10: 0-387-25150-2.
  11. Foertsch, J., Moses, G., Strikwerda, J. and Litzkow, M. (2002), Reversing the Lecture/Homework Paradigm Using eTEACH® Web-based Streaming Video Software. Journal of Engineering Education, 91: 267- 274. doi: 10.1002/j.2168-9830.2002.tb00703.x.
  12. Mavlankar, A; Agrawal, P.; Pang, D.; Halawa, S.; NgaiMan Cheung; Girod, B., "An interactive region-ofinterest video streaming system for online lecture viewing," Packet Video Workshop (PV), 2010 18th International , vol., no., pp.64,71, 13-14 Dec. 2010 doi: 10.1109/PV.2010.5706821.
  13. Cha Zhang, Yong Rui, Jim Crawford, and Li-Wei He. 2008. An automated end-to-end lecture capture and broadcasting system. ACM Trans. Multimedia. Comput. Commun. Appl. 4, 1, Article 6 (February. 2008), 23 pages. DOI=10.1145/1324287.1324293.
  14. Kathryn Faulkner and Linda McClelland. (2002). Using videoconferencing to deliver a healthy education program to women healthy consumers in rural and remote queensland: an early attempt and future plans. Aust. J. Rural Health 10, 65-72.
  15. Dmochowski, Jacek P, Bezdek, Matthew A, Abelson, Brian P, Johnson, John S, Schumacher, Eric H, Parra, Lucas C. 2014. Audience preferences are predicted by temporal reliability of neural processing. Nature Publishing Group, a division of Macmillan Publishers Limited. http://dx.doi.org/10.1038/ncomms5567.10. 1038/ncomms5567.
  16. Chin-Yeh Wang, Gwo-Dong Chen, Chen-Chung Liu, and Baw-Jhiune Liu. 2009. Design an empathic virtual human to encourage and persuade learners in elearning systems. InProceedings of the first ACM international workshop on Multimedia technologies for distance learning (MTDL 7809). ACM, New York, NY, USA, 27-32. DOI=10.1145/1631111.1631117.
  17. Cristina Hava Muntean and Gabriel-Miro Muntean. 2009. Open corpus architecture for personalised ubiquitous. e-learning. Personal Ubiquitous Comput. 13, 3 (March 2009), 197-205. DOI=10.1007/s00779-007-0189-5.
  18. Thomas Wirtky, Sven Laumer, Andreas Eckhardt, and Tim Weitzel. 2013. Using social software for enhancing IS talents' e-learning motivation. In Proceedings of the 2013 annual conference on Computers and people research (SIGMIS-CPR 7813). ACM, New York, NY, USA, 63-72. DOI=10.1145/2487294.2487307.
  19. E. Aljenaa, F. S. Al-Anzi, and M. Alshayeji. 2011. Towards an efficient e-learning system based on cloud computing. In Proceedings of the Second Kuwait Conference on e-Services and e-Systems(KCESS 7811). ACM, New York, NY, USA, Article 13, 7 pages. DOI=10.1145/2107556.2107569.
  20. Vladimir Kolovski and John Galletly. 2003. Towards Elearning via the semantic web. InProceedings of the 4th international conference conference on Computer systems and technologies: e-Learning (CompSysTech 7803), B. Rachev and A. Smrikarov (Eds.). ACM, New York, NY, USA, 591-596. DOI=10.1145/ 973620.973719.
  21. Asmaa Alsumait and Asma Al-Osaimi. 2009. Usability heuristics evaluation for child e-learning applications. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications and Services (iiWAS 7809). ACM, New York, NY, USA, 425-430.
  22. DOI=10.1145/1806338.1806417.
  23. Santoso Handri, Kuniaki Yajima, Shusaku Nomura, Nobuyuki Ogawa, Yoshimasa Kurosawa, and Yoshimi Fukumura. 2010. Evaluation of Student's Physiological Response Towards E-Learning Courses Material by Using GSR Sensor. In Proceedings of the. 2010 IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS 7810). IEEE Computer Society, Washington, DC, USA, 805-810. DOI=10.1109/ICIS.2010.92.
  24. Keith W. Brawner and Benjamin S. Goldberg. 2012. RealTime monitoring of ECG and GSR signals during computer-based training. In Proceedings of the 11th international conference on Intelligent Tutoring Systems (ITS'12), Stefano A. Cerri, William J. Clancey, Giorgos Papadourakis, and Kitty Panourgia (Eds.). Springer-Verlag, Berlin, Heidelberg, 72-77.
  25. DOI=10.1007/978-3-642-30950-2_10.
  26. Sidney D'mello and Art Graesser. 2013. AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Trans. Interact. Intell. Syst. 2, 4, Article 23 (January 2013), 39 pages. DOI=10.1145/ 2395123.2395128.
  27. R. Mandryk. Objectively evaluating entertainment technology. In CHI'04, pages 1057-1058. ACM Press, 2003.
  28. R.W. Picard. Affective computing. MIT Press, Cambridge, MA, USA, 1997.
  29. Shengsheng Ruan, Ling Chen, Jie Sun, and Gencai Chen. 2009. Study on the change of physiological signals during playing body-controlled games. In Proceedings of the International Conference on Advances in Computer Enterntainment Technology (ACE 7809).
  30. ACM, New York, NY, USA, 349-352. DOI=10.1145/1690388.1690456.
  31. Celine Latulipe, Erin A. Carroll, and Danielle Lottridge. 2011. Love, hate, arousal and engagement: exploring audience responses to performing arts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 7811). ACM, New York, NY, USA, 1845-1854. DOI=10.1145/1978942.1979210.
  32. Stephen H. Fairclough. Fundamentals of physiological computing. Interact. Comput. (2009) 21 (1-2): 133- 145.
  33. Robin Kaiser and Karina Oertel. 2006. Emotions in HCI: an affective e-learning system. InProceedings of the HCSNet workshop on Use of vision in humancomputer interaction - Volume 56(VisHCI 7806), Roland Goecke, Antonio Robles-Kelly, and Terry Caelli (Eds.), Vol. 56. Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 105-106.
  34. Chen Wang and Pablo Cesar. 2014, Do we react in the same manner? Comparing GSR patterns across scenarios. NordiCHI 2014.
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Paper Citation


in Harvard Style

Wang C. and Cesar P. (2015). Physiological Measurement on Students’ Engagement in a Distributed Learning Environment . In Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-085-7, pages 149-156. DOI: 10.5220/0005229101490156


in Bibtex Style

@conference{phycs15,
author={Chen Wang and Pablo Cesar},
title={Physiological Measurement on Students’ Engagement in a Distributed Learning Environment},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2015},
pages={149-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005229101490156},
isbn={978-989-758-085-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Physiological Measurement on Students’ Engagement in a Distributed Learning Environment
SN - 978-989-758-085-7
AU - Wang C.
AU - Cesar P.
PY - 2015
SP - 149
EP - 156
DO - 10.5220/0005229101490156