Wei Sun
Xiangmin Fan
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Abstract
The degree and quality of instructor-student interactions are crucial for students' engagement, retention, and learning outcomes. However, such interactions are limited in live online lectures, where instructors no longer have access to important cues such as raised hands or facial expressions at the time of teaching. As a result, instructors cannot fully understand students' learning progresses. This paper presents an explorative study investigating how presenters perceive and react to audience flow prediction when giving live-stream lectures, which has not been examined yet. The study was conducted with an experimental system that can predict audience's psychological states (e.g., anxiety, flow, boredom) through real-time facial expression analysis, and can provide aggregated views illustrating the flow experience of the whole group. Through evaluation with 8 online lectures (N_instructors=8, N_learners=21), we found such real-time flow prediction and visualization can provide value to presenters. This paper contributes a set of useful findings regarding their perception and reaction of such flow prediction, as well as lessons learned in the study, which can be inspirational for building future AI-powered system to assist people in delivering live online presentations.
- 2012. InstFeedback. http://instfeedback.com.Google Scholar
- 2018. By The Numbers: MOOCS in 2017. https://www.classcentral.com/report/mooc-stats-2017/.Google Scholar
- 2019. Affectiva. https://www.affectiva.com/.Google Scholar
- 2019. KMF online learning platform. https://www.kmf.com/.Google Scholar
- 2019. New Oriental Education and Technology Group. http://www.neworiental.org/english/.Google Scholar
- Daniel Afergan, Evan M Peck, Erin T Solovey, Andrew Jenkins, Samuel W Hincks, Eli T Brown, Remco Chang, and Robert JK Jacob. 2014. Dynamic difficulty using brain metrics of workload. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3797--3806.Google ScholarDigital Library
- Daniel Afergan, Tomoki Shibata, Samuel W Hincks, Evan M Peck, Beste F Yuksel, Remco Chang, and Robert JK Jacob. 2014. Brain-based target expansion. In Proceedings of the 27th annual ACM symposium on User interface software and technology. ACM, 583--593.Google ScholarDigital Library
- Jeremy N Bailenson, Emmanuel D Pontikakis, Iris B Mauss, James J Gross, Maria E Jabon, Cendri AC Hutcherson, Clifford Nass, and Oliver John. 2008. Real-time classification of evoked emotions using facial feature tracking and physiological responses. International journal of human-computer studies 66, 5 (2008), 303--317.Google Scholar
- Ashok R Basawapatna, Alexander Repenning, Kyu Han Koh, and Hilarie Nickerson. 2013. The zones of proximal flow: guiding students through a space of computational thinking skills and challenges. In Proceedings of the ninth annual international ACM conference on International computing education research. ACM, 67--74.Google ScholarDigital Library
- AJ Brush, David Bargeron, Jonathan Grudin, Alan Borning, and Anoop Gupta. 2002. Supporting interaction outside of class: anchored discussions vs. discussion boards. In Proceedings of the Conference on Computer Support for Collaborative Learning: Foundations for a CSCL Community. International Society of the Learning Sciences, 425--434.Google ScholarCross Ref
- Isabel Buil, Sara Catalán, and Eva Martínez. 2019. The influence of flow on learning outcomes: An empirical study on the use of clickers. British Journal of Educational Technology 50, 1 (2019), 428--439.Google ScholarCross Ref
- Di Laura Chen, Dustin Freeman, and Ravin Balakrishnan. 2019. Integrating Multimedia Tools to Enrich Interactions in Live Streaming for Language Learning. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 438.Google ScholarDigital Library
- Mihalyi Csikszentmihalyi. 2014. Flow and the foundations of positive psychology. Recuperado de http://www. springer. com/us/book/9789401790871 (2014).Google Scholar
- Mihaly Csikszentmihalyi and Isabella Csikszentmihalyi. 1975. Beyond boredom and anxiety. Vol. 721. Jossey-Bass San Francisco.Google Scholar
- Mihaly Csikszentmihalyi and Isabella Selega Csikszentmihalyi. 1992. Optimal experience: Psychological studies of flow in consciousness. Cambridge university press.Google Scholar
- Travis Faas, Lynn Dombrowski, Alyson Young, and Andrew D Miller. 2018. Watch me code: programming mentorship communities on Twitch. tv. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 50.Google ScholarDigital Library
- Elena L Glassman, Juho Kim, Andrés Monroy-Hernández, and Meredith Ringel Morris. 2015. Mudslide: A spatially anchored census of student confusion for online lecture videos. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 1555--1564.Google ScholarDigital Library
- Mariam Hassib, Stefan Schneegass, Philipp Eiglsperger, Niels Henze, Albrecht Schmidt, and Florian Alt. 2017. EngageMeter: A system for implicit audience engagement sensing using electroencephalography. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 5114--5119.Google ScholarDigital Library
- Joel Hektner, Jennifer Schmidt, and Mihaly Csikszentmihalyi. 2019. Experience Sampling Method. Thousand Oaks, California. https://doi.org/10.4135/9781412984201Google Scholar
- Javier Hernandez, Zicheng Liu, Geoff Hulten, Dave DeBarr, Kyle Krum, and Zhengyou Zhang. 2013. Measuring the engagement level of TV viewers. In 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 1--7.Google ScholarCross Ref
- Juho Kim, Philip J Guo, Daniel T Seaton, Piotr Mitros, Krzysztof Z Gajos, and Robert C Miller. 2014. Understanding in-video dropouts and interaction peaks inonline lecture videos. In Proceedings of the first ACM conference on Learning@ scale conference. ACM, 31--40.Google ScholarDigital Library
- Kyu Han Koh, Ashok Basawapatna, Hilarie Nickerson, and Alexander Repenning. 2014. Real time assessment of computational thinking. In 2014 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, 49--52.Google ScholarCross Ref
- Yang Li, Samy Bengio, and Gilles Bailly. 2018. Predicting human performance in vertical menu selection using deep learning. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 29.Google ScholarDigital Library
- Zhicong Lu, Seongkook Heo, and Daniel J Wigdor. 2018. StreamWiki: Enabling Viewers of Knowledge Sharing Live Streams to Collaboratively Generate Archival Documentation for Effective In-Stream and Post Hoc Learning. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 112.Google ScholarDigital Library
- Rajitha Navarathna, Patrick Lucey, Peter Carr, Elizabeth Carter, Sridha Sridharan, and Iain Matthews. 2014. Predicting movie ratings from audience behaviors. In IEEE Winter Conference on Applications of Computer Vision. IEEE, 1058--1065.Google ScholarCross Ref
- Thao Nguyen, Iris Bass, Mingkun Li, and Ishwar K Sethi. 2005. Investigation of combining SVM and decision tree for emotion classification. In Seventh IEEE International Symposium on Multimedia (ISM'05). IEEE, 5--pp.Google ScholarDigital Library
- Anna Nowicka, Artur Marchewka, Katarzyna Jednorog, Pawel Tacikowski, and Andre Brechmann. 2010. Forgetting of emotional information is hard: an fMRI study of directed forgetting. Cerebral Cortex 21, 3 (2010), 539--549.Google ScholarCross Ref
- Phuong Pham and Jingtao Wang. 2015. AttentiveLearner: improving mobile MOOC learning via implicit heart rate tracking. In International Conference on Artificial Intelligence in Education. Springer, 367--376.Google ScholarCross Ref
- Phuong Pham and Jingtao Wang. 2017. AttentiveLearner 2: a multimodal approach for improving MOOC learning on mobile devices. In International Conference on Artificial Intelligence in Education. Springer, 561--564.Google ScholarCross Ref
- Phuong Pham and Jingtao Wang. 2017. Understanding emotional responses to mobile video advertisements via physiological signal sensing and facial expression analysis. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM, 67--78.Google ScholarDigital Library
- Phuong Pham and Jingtao Wang. 2018. Adaptive Review for Mobile MOOC Learning via Multimodal Physiological Signal Sensing-A Longitudinal Study. In Proceedings of the 2018 on International Conference on Multimodal Interaction. ACM, 63--72.Google ScholarDigital Library
- David J Shernoff and Mihaly Csikszentmihalyi. [n. d.]. Cultivating Engaged Learners and Optimal Learning Environments. ([n. d.]), 16.Google Scholar
- David J. Shernoff, Mihaly Csikszentmihalyi, Barbara Shneider, and Elisa Steele Shernoff. 2003. Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly 18, 2 (2003), 158--176. https://doi.org/10.1521/scpq.18.2.158.21860Google ScholarCross Ref
- Hyungyu Shin, Eun-Young Ko, Joseph Jay Williams, and Juho Kim. 2018. Understanding the Effect of In-Video Prompting on Learners and Instructors. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 319.Google ScholarDigital Library
- M Iftekhar Tanveer, Samiha Samrose, Raiyan Abdul Baten, and M Ehsan Hoque. 2018. Awe the Audience: How the Narrative Trajectories Affect Audience Perception in Public Speaking. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 24.Google ScholarDigital Library
- Julianne C Turner, Kathleen E Cox, Matthew DiCintio, Debra K Meyer, Candice Logan, and Cynthia T Thomas. [n. d.]. Creating Contexts for Involvement in Mathematics. ([n. d.]), 16.Google Scholar
- Johannes Wagner, Jonghwa Kim, and Elisabeth André. 2005. From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In 2005 IEEE international conference on multimedia and expo. IEEE, 940--943.Google ScholarCross Ref
- Jacob Whitehill, Marian Bartlett, and Javier Movellan. 2008. Measuring the perceived difficulty of a lecture using automatic facial expression recognition. In International Conference on Intelligent Tutoring Systems. Springer, 668--670.Google ScholarDigital Library
- Xiang Xiao and Jingtao Wang. 2016. Context and cognitive state triggered interventions for mobile MOOC learning. In Proceedings of the 18th ACM International Conference on Multimodal Interaction. ACM, 378--385.Google ScholarDigital Library
- Chun yan Nie, JuWang, Fang He, and Reika Sato. 2015. Application of J48 decision tree classifier in emotion recognition based on chaos characteristics. In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press.Google ScholarCross Ref
- Chi-Lan Yang, Chien Wen (Tina) Yuan, Tzu-Yang Wang, and Hao-Chuan Wang. 2018. Knowing That You Know What I Know Helps?: Understanding the Effects of Knowledge Transparency in Online Knowledge Transfer. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 189 (Nov. 2018), 21 pages. https://doi.org/10.1145/3274458Google ScholarDigital Library
- Beste F Yuksel, Kurt B Oleson, Lane Harrison, Evan M Peck, Daniel Afergan, Remco Chang, and Robert JK Jacob. 2016. Learn piano with BACh: An adaptive learning interface that adjusts task difficulty based on brain state. In Proceedings of the 2016 chi conference on human factors in computing systems. ACM, 5372--5384.Google ScholarDigital Library
- Wei-Long Zheng,Wei Liu, Yifei Lu, Bao-Liang Lu, and Andrzej Cichocki. 2018. Emotionmeter: A multimodal framework for recognizing human emotions. IEEE transactions on cybernetics 99 (2018), 1--13.Google Scholar
- Sacha Zyto, David Karger, Mark Ackerman, and Sanjoy Mahajan. 2012. Successful classroom deployment of a social document annotation system. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1883--1892.Google ScholarDigital Library
Index Terms
- How Presenters Perceive and React to Audience Flow Prediction In-situ: An Explorative Study of Live Online Lectures
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