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Research Article

Detection of Affective States of the Students in a Blended Learning Environment Comprising of Smartphones

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  • AlZoubi, O., D’Mello, S. K., & Calvo, R. A. (2012). Detecting naturalistic expressions of nonbasic affect using physiological signals. IEEE Transactions on Affective Computing, 3(3), 298–310. https://doi.org/10.1109/T-AFFC.2012.4
  • Anolli, L., Mantovani, F., Confalonieri, L., Ascolese, A., & Peveri, L. (2010). Emotions in serious games: From experience to assessment. International Journal of Emerging Technologies in Learning (Ijet), 5(2010), 7–16. https://doi.org/10.3991/ijet.v5s3.1496
  • Ashwin, T.S., & Guddeti, R. M. R. (2020). Automatic detection of students' affective states in classroom environment using hybrid convolutional neural networks. Education and Information Technologies, 25(2), 1387–1415.
  • Bartneck, C., & Reichenbach, J. (2005). Subtle emotional expressions of synthetic characters. International Journal of Human-computer Studies, 62(2), 179–192. https://doi.org/10.1016/j.ijhcs.2004.11.006
  • Bartram, L., Patra, A., & Stone, M. (2017, May). Affective color in visualization. In Proceedings of the 2017 CHI conference on human factors in computing systems (CHI 2017) (pp. 1364–1374). ACM.
  • Bauer, G., & Lukowicz, P. (2012, March). Can smartphones detect stress-related changes in the behaviour of individuals? In 2012 IEEE international conference on pervasive computing and communications workshops (pp. 423–426). IEEE.
  • Beedie, C., Terry, P., & Lane, A. (2005). Distinctions between emotion and mood. Cognition & Emotion, 19(6), 847–878. https://doi.org/10.1080/02699930541000057
  • Boonroungrut, C., & Oo, T. T. (2019). Exploring classroom emotion with cloud-based facial recognizer in the Chinese beginning class: A preliminary study. International Journal of Instruction, 12(1), 947–958
  • Bosch, N., & D'Mello, S. (2019). Automatic detection of mind wandering from video in the lab and in the classroom. IEEE Transactions on Affective Computing.
  • Botelho, A. F., Baker, R. S., & Heffernan, N. T. (2017, June). Improving sensor-free affect detection using deep learning. In International conference on artificial intelligence in education (pp. 40–51). Springer.
  • Camurri, A., Lagerlöf, I., & Volpe, G. (2003). Recognizing emotion from dance movement: Comparison of spectator recognition and automated techniques. International Journal of Human-computer Studies, 59(1–2), 213–225. https://doi.org/10.1016/S1071-5819(03)00050-8
  • Chen, Y., Gao, Q., Yuan, Q., & Tang, Y. (2019). Facilitating students’ interaction in MOOCs through timeline-anchored discussion. International Journal of Human-Computer Interaction, 35(19), 1781–1799. https://doi.org/10.1080/10447318.2019.1574056
  • Ciman, M., Wac, K., & Gaggi, O. (2015, May). iSenseStress: Assessing stress through human-smartphone interaction analysis. In 2015 9th international conference on pervasive computing technologies for healthcare (PervasiveHealth 2015) (pp. 84–91). IEEE.
  • Collier, G. L. (2007). Beyond valence and activity in the emotional connotations of music. Psychology of Music, 35(1), 110–131. https://doi.org/10.1177/0305735607068890
  • D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., Person, N., Kort, B., El Kaliouby, R., Picard, R., & Graesser, A. (2008, June). AutoTutor detects and responds to learners affective and cognitive states. In Workshop on emotional and cognitive issues at the international conference on intelligent tutoring systems (pp. 306–308).
  • Dao, M. S., Dang Nguyen, D. T., & Kasem, A. (2018). HealthyClassroom - a proof-of-concept study for discovering students’ daily moods and classroom emotions to enhance a learning-teaching process using heterogeneous sensors. In The 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2018). (pp. 685–691). Funchal, Madeira, Portugal.
  • dBaker, R. S., Gowda, S. M., Wixon, M., Kalka, J., Wagner, A. Z., Salvi, A., Aleven, V., Kusbit, G. W., Ocumpaugh, J., & Rossi, L. (2012, June). Towards sensor-free affect detection in cognitive tutor algebra. K. Yacef, O. Zaïane, A. Hershkovitz, M. Yudelson, & J. Stamper (Eds), In Proceedings of the 5th international conference on educational data mining, international educational data mining society. (pp. 126–133).
  • Debener, S., Minow, F., Emkes, R., Gandras, K., & De Vos, M. (2012). How about taking a low‐cost, small, and wireless EEG for a walk? Psychophysiology, 49(11), 1617–1621. https://doi.org/10.1111/j.1469-8986.2012.01471.x
  • Douglas-Cowie, E., Cowie, R., Sneddon, I., Cox, C., Lowry, O., Mcrorie, M., Martin, J. C., Devillers, L., Abrilian, S., Batliner, A., & Amir, N. (2007, September). The HUMAINE database: Addressing the collection and annotation of naturalistic and induced emotional data. In International conference on affective computing and intelligent interaction (pp. 488–500). Springer.
  • Epp, C., Lippold, M., & Mandryk, R. L. (2011, May). Identifying emotional states using keystroke dynamics. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI 2011), (pp. 715–724). ACM.
  • Fu, J., Ge, T., Li, M., & Hu, X. (2019). Affective computation of students’ behaviors under classroom scenes. In NeuroManagement and Intelligent Computing Method on Multimodal Interaction (pp. 1–6). Suzhou, Jiangsu, China.
  • Gabrielsson, A. (2001). Emotion perceived and emotion felt: Same or different? Musicae Scientiae, 5(1_suppl), 123–147. https://doi.org/10.1177/10298649020050S105
  • Gaffary, Y., Jáuregui, D. A. G., Martin, J. C., & Ammi, M. (2015, September). Gestural and postural reactions to stressful event: Design of a haptic stressful stimulus. In 2015 international conference on Affective Computing and Intelligent Interaction (ACII 2015) (pp. 988–992). IEEE.
  • Gan, C. L., & Balakrishnan, V. (2018). Mobile technology in the classroom: What drives student-lecturer interactions? International Journal of Human-Computer Interaction, 34(7), 666–679. https://doi.org/10.1080/10447318.2017.1380970
  • Gao, Y., Bianchi-Berthouze, N., & Meng, H. (2012). What does touch tell us about emotions in touchscreen-based gameplay? ACM Transactions on Computer-Human Interaction (TOCHI), 19(4), 1–30. https://doi.org/10.1145/2395131.2395138
  • Gilleade, K. M., & Dix, A. (2004, September). Using frustration in the design of adaptive videogames. In Proceedings of the 2004 ACM SIGCHI international conference on advances in computer entertainment technology (pp. 228–232). ACM, National University of Singapore.
  • Gitinabard, N., Xu, Y., Heckman, S., Barnes, T., & Lynch, C. F. (2019). How widely can prediction models be generalized? Performance prediction in blended courses. IEEE Transactions on Learning Technologies, 12(2), 184–197. https://doi.org/10.1109/TLT.4620076
  • Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., & Scherer, K. (2011). Toward a minimal representation of affective gestures. IEEE Transactions on Affective Computing, 2(2), 106–118. https://doi.org/10.1109/T-AFFC.2011.7
  • González-Gómez, D., Jeong, J. S., & Rodríguez, D. A. (2016). Performance and perception in the flipped learning model: An initial approach to evaluate the effectiveness of a new teaching methodology in a general science classroom. Journal of Science Education and Technology, 25(3), 450–459. https://doi.org/10.1007/s10956-016-9605-9
  • Hammerla, N. Y., & Plötz, T. (2015, September). Let’s (not) stick together: Pairwise similarity biases cross-validation in activity recognition. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (UbiComp 2015) (pp. 1041–1051). ACM.
  • Hazlett, R. L. (2006, April). Measuring emotional valence during interactive experiences: Boys at video game play. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI 2006) (pp. 1023–1026). ACM.
  • Healey, J. A., & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2), 156–166. https://doi.org/10.1109/TITS.2005.848368
  • Hertenstein, M. J., Holmes, R., McCullough, M., & Keltner, D. (2009). The communication of emotion via touch. Emotion, 9(4), 566–573. https://doi.org/10.1037/a0016108
  • Kambouropoulos, N., & Staiger, P. K. (2004). Personality and responses to appetitive and aversive stimuli: The joint influence of behavioural approach and behavioural inhibition systems. Personality and Individual Differences, 37(6), 1153–1165. https://doi.org/10.1016/j.paid.2003.11.019
  • Khanna, P., & Sasikumar, M. (2010). Recognising emotions from keyboard stroke pattern. International Journal of Computer Applications, 11(9), 1–5. https://d1wqtxts1xzle7.cloudfront.net/45795162/Recognising_Emotions_from_Keyboard_Strok20160519-7171-2jl0y0.pdf?1463727010=&response-content-disposition=inline%3B+filename%3DRecognising_Emotions_from_Keyboard_Strok.pdf&Expires=1608130508&Signature=K9D6npQI4lC7L4ZD7QWHFfpGfYAb83tBtBtFZ1Cv74Drqfjr~slpfd3LQVPhw1Eg34gpYD6nrOK2RbA7oQ~NO0fxPL41rKa7cgFZn2upccsxPuocqIkgWf29MdAOlGdXQj6P1y3AT-NLdXwoQAuBDwsSjiwjBUPBozV5-ADPz1Jd8EcF~pq4qZvd46lkJETJbtTHsri~5ozw0Z4zN~QURiN-DipLYGmCH1qSE6NT43RkbuHFL~S8G5-CW62iEQRJj8aeKPslYSecDMaPkRhCp5cim-gjMrSdzzu5F5JX70FOXIYdusCX9NG-ID6zbo725ioqb3I1OQs-tfK798p~sA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
  • Kim, Y., Soyata, T., & Behnagh, R. F. (2018). Towards emotionally aware AI smart classroom: Current issues and directions for engineering and education. IEEE Access, 6, 5308–5331. https://doi.org/10.1109/ACCESS.2018.2791861
  • Kolodyazhniy, V., Kreibig, S. D., Gross, J. J., Roth, W. T., & Wilhelm, F. H. (2011). An affective computing approach to physiological emotion specificity: Toward subject‐independent and stimulus‐independent classification of film‐induced emotions. Psychophysiology, 48(7), 908–922. https://doi.org/10.1111/j.1469-8986.2010.01170.x
  • Kwet, M., & Prinsloo, P. (2020). The ‘smart’classroom: a new frontier in the age of the smart university. Teaching in Higher Education, 25(4), 510–526. https://doi.org/10.1080/13562517.2020.1734922
  • Lang, P. J., Greenwald, M. K., Bradley, M. M., & Hamm, A. O. (1993). Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30(3), 261–273. https://doi.org/10.1111/j.1469-8986.1993.tb03352.x
  • Lee, H., Choi, Y. S., Lee, S., & Park, I. P. (2012, January). Towards unobtrusive emotion recognition for affective social communication. In 2012 IEEE Consumer Communications and Networking Conference (CCNC 2012) (pp. 260–264). IEEE.
  • Lee, P. M., Tsui, W. H., & Hsiao, T. C. (2014). The influence of emotion on keyboard typing: An experimental study using visual stimuli. Biomedical Engineering Online, 13(1), 81–92. https://doi.org/10.1186/1475-925X-13-81
  • Li, J., Shi, D., Tumnark, P., & Xu, H. (2020). A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure. Peer-to-Peer Networking and Applications, pp. 1–14. https://doi.org/10.1007/s12083-019-00863-8
  • Li, L., Cheng, L., & Qian, K. X. (2008, September). An e-learning system model based on affective computing. In 2008 international conference on cyberworlds (pp. 45–50). IEEE.
  • Lim, Y. M., Ayesh, A., & Stacey, M. (2014, August). The effects of typing demand on emotional stress, mouse and keystroke behaviours. In Science and information conference (pp. 209–225). Springer.
  • Lim, Y. M., Ayesh, A., & Stacey, M. (2020). Continuous stress monitoring under varied demands using unobtrusive devices. International Journal of Human–Computer Interaction, 36(4), 326–340. https://doi.org/10.1080/10447318.2019.1642617
  • Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. J. Davidson, K. R. Shekerer, & H. H. Goldsmith (Eds.), Handbook of affective science (pp. 619–642). Oxford University Press.
  • Lv, H. R., Lin, Z. L., Yin, W. J., & Dong, J. (2008, June). Emotion recognition based on pressure sensor keyboards. In 2008 IEEE international conference on multimedia and expo (pp. 1089–1092). IEEE.
  • Matsuda, Y., Sakuma, I., Jimbo, Y., Kobayashi, E., Arafune, T., & Isomura, T. (2010). Emotional communication in finger braille. Advances in Human-Computer Interaction, 2010(4), 1–23. https://doi.org/10.1155/2010/830759
  • Miller, M. K., & Mandryk, R. L. (2016, November). Differentiating in-game frustration from at-game frustration using touch pressure. In Proceedings of the 2016 ACM international conference on interactive surfaces and spaces (pp. 225–234). ACM.
  • Murray, I. R., & Arnott, J. L. (1993). Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion. The Journal of the Acoustical Society of America, 93(2), 1097–1108. https://doi.org/10.1121/1.405558
  • Nahin, A. N. H., Alam, J. M., Mahmud, H., & Hasan, K. (2014). Identifying emotion by keystroke dynamics and text pattern analysis. Behaviour & Information Technology, 33(9), 987–996. https://doi.org/10.1080/0144929X.2014.907343
  • Nortvig, A. M., Petersen, A. K., & Balle, S. H. (2018). A literature review of the factors influencing E-learning and blended learning in relation to learning outcome, student satisfaction and engagement. Electronic Journal of E-learning, 16(1), 46–55. https://files.eric.ed.gov/fulltext/EJ1175336.pdf
  • Paquette, L., Baker, R. S., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., & Kauffman-Rogoff, Z. (2014, June). Sensor-free affect detection for a simulation-based science inquiry learning environment. In International conference on intelligent tutoring systems (pp. 1–10). Springer.
  • Philippot, P. (1993). Inducing and assessing differentiated emotion-feeling states in the laboratory. Cognition and Emotion, 7(2), 171–193. https://doi.org/10.1080/02699939308409183
  • Polivy, J. (1981). On the induction of emotion in the laboratory: Discrete moods or multiple affect states? Journal of Personality and Social Psychology, 41(4), 803–817. https://doi.org/10.1037/0022-3514.41.4.803
  • Popescu, R., Ponescu, D., Roibu, H., & Popescu, L.C. (2018, September). Smart classroom-affective computing in present-day classroom. In 2018 28th EAEEIE Annual Conference (EAEEIE) (pp. 1–9). Hafnarfjordur, Iceland.
  • Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17(3), 715–734. https://doi.org/10.1017/S0954579405050340
  • Rao, K. S., Kumar, T. P., Anusha, K., Leela, B., Bhavana, I., & Gowtham, S. V. S. K. (2012). Emotion recognition from speech. International Journal of Computer Science and Information Technologies, 3(2), 3603–3607. http://cloud.politala.ac.id/politala/1.%20Jurusan/Teknik%20Informatika/19.%20e-journal/Jurnal%20Internasional%20TI/IJCSIT/Vol%203/ISSUE%202/ijcsit2012030264.pdf
  • Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714
  • Sano, A., & Picard, R. W. (2013, September). Stress recognition using wearable sensors and mobile phones. In 2013 humaine association conference on affective computing and intelligent interaction (pp. 671–676). IEEE.
  • Savvaki, C., Leonidis, A., Paparoulis, G., Antona, M., & Stephanidis, C. (2013, July). Designing a technology–augmented school desk for the future classroom. In International Conference on Human-Computer Interaction (pp. 681–685). Berlin, Heidelberg, Germany.
  • Shah, S., Teja, J. N., & Bhattacharya, S. (2015). Towards affective touch interaction: Predicting mobile user emotion from finger strokes. Journal of Interaction Science, 3(1), 1–15. https://doi.org/10.1186/s40166-015-0013-z
  • Söderlund, M., & Rosengren, S. (2008). Revisiting the smiling service worker and customer satisfaction. International Journal of Service Industry Management., 19(5), 552–574. https://doi.org/10.1108/09564230810903460
  • Sokolova, M. V., & Fernández-Caballero, A. (2015). A review on the role of color and light in affective computing. Applied Sciences, 5(3), 275–293. https://doi.org/10.3390/app5030275
  • Stein, A., Yotam, Y., Puzis, R., Shani, G., & Taieb-Maimon, M. (2018). EEG-triggered dynamic difficulty adjustment for multiplayer games. Entertainment Computing, 25(2018), 14–25. https://doi.org/10.1016/j.entcom.2017.11.003
  • Tikadar, S., Bhattacharya, S., & Tamarapalli, V. (2018, July). A blended learning platform to improve teaching-learning experience. In 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT 2018) (pp. 87–89). IEEE.
  • Västfjäll, D. (2001). Emotion induction through music: A review of the musical mood induction procedure. Musicae Scientiae, 5(1_suppl), 173–211. https://doi.org/10.1177/10298649020050S107
  • Vicencio-Moreira, R., Mandryk, R. L., & Gutwin, C. (2015, April). Now you can compete with anyone: Balancing players of different skill levels in a first-person shooter game. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 2255–2264). ACM.
  • Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870–886. https://doi.org/10.1016/j.ijhcs.2009.07.005
  • Wahyono, I. D., Saryono, D., Ashar, M., & Asfani, K. (2019, September). Face emotional detection using computational intelligence based ubiquitous computing. In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), (pp. 389–393). Semarang, Jawa Tengah, Indonesia.
  • Whitehill, J., Serpell, Z., Lin, Y.C., Foster, A., & Movellan, J.R. (2014). The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions. IEEE Transactions on Affective Computing, 5(1), 86–98. https://doi.org/10.1109/TAFFC.2014.2316163
  • Woolf, B., Burelson, W., & Arroyo, I. (2007, July). Emotional intelligence for computer tutors. In Workshop on modeling and scaffolding affective experiences to impact learning at 13th international conference on artificial intelligence in education, (pp. 6–15).
  • Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: Recognising and responding to student affect. International Journal of Learning Technology, 4(3–4), 129–164. https://doi.org/10.1504/IJLT.2009.028804
  • Yannakakis, G. N., & Pavia, A. (2014). Emotion in games. In J. G. D’Mello & A. Kappas (Eds.), Handbook on affective computing (Vol. 2014, pp. 459–471). Oxford University Press.
  • Zimmermann, P., Guttormsen, S., Danuser, B., & Gomez, P. (2003). Affective computing—a rationale for measuring mood with mouse and keyboard. International Journal of Occupational Safety and Ergonomics, 9(4), 539–551. https://doi.org/10.1080/10803548.2003.11076589

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