A Ubiquitous Model of Emotional Tracking in Virtual Classes: From Simple Emotions to Learning Action Tendency
Abstract
Researchers in the field of ubiquitous learning assert that emotional presence management leads to promising results in the teaching-learning process. However, are there predominant emotions in virtual classes? How can emotional clusters be created? Is it possible to obtain action tendencies in virtual classes? In response to these questions, we propose a ubiquitous emotional model for virtual classes, recording the simple emotions from participants´ faces, checking whether these emotions can produce emotional clusters, and using these clusters to infer action tendencies within the context of virtual classes. The experimentation was carried out in a real virtual class. To evaluate the model, the experts verified the results of the ubiquitous model, and it is confirmed that the action tendency obtained by the model coincides with the criteria of the experts. Inferring these action tendencies are very important, since the teacher, unlike a face-to-face model, has difficulty observing all the students with the use of cameras in virtual classes, making it difficult to understand the student´s behavior online.
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S. Z. Salas-Pilco, “The Impact of COVID-19 on Latin American STEM Higher Education: A Systematic Review,” presented at the IEEE World Eng. Educ. Conf. (EDUNINE), Santos, Brazil, March. 13-16, 2022.
C. A. Collazos et al., “The use of e-learning platforms in a lockdown scenario—A study in Latin American countries,” IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 16, no.4, pp. 419-423, 2021.
R. Barragan-Quintero et al., “The impact of digitalization in the Latin American Wine Industry during the Covid-19 Pandemic,” presented at the IEEE Int. Conf. Technol. and Entrepreneurship (ICTE), Kaunas, Lithuania, Aug. 24-27, 2021.
J. Salmi, “Tertiary education in the twenty-first century: Challenges and opportunities,” The World Bank, LCSHD Paper Series, 2000, vol. 62, pp. 1-27.
K. Okoye et al., “Impact of digital technologies upon teaching and learning in higher education in Latin America: an outlook on the reach, barriers, and bottlenecks,” Educ. Inf. Technol., Aug. 2022, doi: 10.1007/s10639-022-11214-1.
S. Delgado et al., “Analysis of Students’ Behavior through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks,” IEEE Access, vol. 9, pp. 132592-132608, 2021, doi: 10.1109/ACCESS.2021.3115024.
B. C. Ko, “A brief review of facial emotion recognition based on visual information,” Sensors, vol. 18, no. 2, p.401, 2018, doi: 10.3390/s18020401.
P. Sharma et al., “Student concentration evaluation index in an e-learning context using facial emotion analysis,” Commun. Comput. Inf. Science, vol. 993, 2019, doi: 10.1007/978-3-030-20954-4_40.
S. Marcos-Pablos, F. L. Alejano, and F. J. García-Peñalvo, “Integrating Emotion Recognition Tools for Developing Emotionally Intelligent Agents,” Int. J. Interact. Multimedia Artif. Intell., vol. 7, no. 6, pp. 69–76, 2022, doi: 10.9781/ijimai.2022.09.004.
C. Heckel, and T. Ringeisen, “Pride and anxiety in online learning environments: Achievement emotions as mediators between learners' characteristics and learning outcomes,” J. Comput. Assisted Learn., vol. 35, no. 5, pp. 667-677, 2019.
D. Palmer, “The action tendency for learning: Characteristics and antecedents in regular lessons,” Int. J. Educational. Res., vol. 82, pp. 99-109, 2017.
M. G. Huddar, S. S. Sannakki, and V. S. Rajpurohit, “Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN,” Int. J. Interact. Multimedia Artif. Intell., vol. 6, no. 6, pp. 112-121, 2021, https://doi.org/10.9781/ijimai.2020.07.004.
L. Pham and A. N. Q. Phan, “Let’s accept it: Vietnamese university language teachers’ emotion in online synchronous teaching in response to COVID-19,” Educational and Developmental Psychologist, vol. 40, no.1, pp. 115-124, 2023.
R. Z. Cabada, M. L. Barron, and M. Cárdenas López, “Reconocimiento multimodal de emociones orientadas al aprendizaje,” Res. Comput. Sci., vol. 148, no. 7, pp. 153-165, 2019.
G. Matthews, “Developing Emotionally Intelligent Teachers: A Panacea for Quality Teacher Education,” Int. J. Integr. Educ., vol. 3, no. 6, pp.92-98, 2020, doi: 10.31149/ijie.v3i10.676.
T. Gneiting, and P. Vogel, “Receiver operating characteristic (ROC) curves,” Mach. Learn., 2018.
H. B. Harvey and S. T. Sotardi, “The Pareto Principle,” J. Amer. College Radiol., vol. 15, no. 6, p. 931, 2018, doi: 10.1016/j.jacr.2018.02.026.
Y. Quan, “Development of computer aided classroom teaching system based on machine learning prediction and artificial intelligence KNN algorithm,” J. Intell. Fuzzy Syst., vol. 39, no. 2, pp. 1879–1890, 2020, doi: 10.3233/JIFS-179959.
R. Islam et al., “Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique,” presented at the 2018 Int. Conf. Computer, Commun., Chem., Mater. Electron. Eng. (IC4ME2), Rajshahi, IEEE, 2018.
R. Hufendiek, “Emotions, Habits, and Skills,” Habits, pp. 100-119, 2020, doi: 10.1017/9781108682312.005.
S. C. Koch, “Being moved as a therapeutic factor of dance movement therapy,” Dance Creativity Dance Movement Therapy: Int. Perspectives, pp. 96-110, 2020. doi: 10.4324/9780429442308-10.
I. Wiegman, “Emotional Actions Without Goals,” Erkenntnis, vol. 87, no. 1, pp. 393–423, Feb. 2022, doi: 10.1007/s10670-019-00200-8.
C. Obeid, I. Lahoud, H. El Khoury, and P. A. Champin, “Ontology-based Recommender System in Higher Education,” in Companion Proc. Web Conf., 2018. doi: 10.1145/3184558.3191533.
A. Raes et al., “Learning and instruction in the hybridation virtual classroom: An investigation of students’ engagement and the effect of quizzes,” Computers & Education, vol. 143, p.103682, Jan. 2020, doi: 10.1016/j.compedu.2019.103682.
S. McLeod, “Pareto Principle,” Simply Psychology, 2018.
A. Ampudia Rueda, “Precisión diagnóstica del MMPI-2 con la personalidad delictiva: un análisis con la curva ROC,” Revista de Psicología (PUCP), vol. 35, no. 1, pp. 167-192, 2017.
D. Mahapatra, and V. Rajan, “Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization,” presented at the Int. Conf. Machine Learn., 2020, pp.6597-6607.
R. J. Vallerand, and C. M. Blanchard, “The study of emotion in sport and exercise: Historical, definitional, and conceptual perspectives,” In Y. L. Hanin (Ed.), Emotions Sport, pp. 3-37, 2000.
A. Petrova, D. Vaufreydaz, and P. Dessus, “Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach,” in Proc. Int. Conf. Multimodal Interaction (ICMI), Oct. 2020, pp. 813–820. doi: 10.1145/3382507.3417969.
I. Anzelin, A. Marín-Gutiérrez, and J. Chocontá, “Relationship between emotion and teaching-learning processes: state of knowledge,” SOPHIA, vol. 16, no. 1, 2020, doi: 10.18634/sophiaj.16v.1i.1007.
B. L. Kramer, “Effect of Emotional Intelligence, Collaboration Technology, Team Climate, and Intrinsic Motivation on Virtual Team Effectiveness: A Study of Team Member Perceptions,” Ph. D. dissertation, Eastern Michigan Univ., USA, 2020.
R. Feyzi Behnagh, “Emotions and emotional energy in the science classroom: A discussion of measurement,” Cultural Stud. Science Educ., vol. 15, no. 1, pp. 307–315, Mar. 2020, doi: 10.1007/s11422-019-09929-8.
S. Cobos-Guzman, S. Nuere, L. de Miguel, and C. König, “Design of a virtual assistant to improve interaction between the audience and the presenter,” Int. J. Interact. Multimedia Artif. Intell., vol. 7, no. 2, 2021, doi: 10.9781/ijimai.2021.08.017.
R. Colomo-Palacios, C. Casado-Lumbreras, J. M. Álvarez-Rodríguez, and M. Yilmaz, “Coding vs presenting: a multicultural study on emotions,” Inf. Technol. People, vol. 33, no. 6, pp. 1575–1599, Oct. 2020, doi: 10.1108/ITP-12-2019-0633.
J. A. Rattel, I. B. Mauss, M. Liedlgruber, and F. H. Wilhelm, “Sex differences in emotional concordance,” Biological Psychol., vol. 151, Mar. 2020, doi: 10.1016/j.biopsycho.2020.107845.
A. Kaviani, “On the Significance of Students’ Appraisals of Their Language Learning Experiences at University: A Phenomenological Approach,” J. Educ. Social Res., vol. 10, no. 6, pp. 225–236, Nov. 2020, doi: 10.36941/jesr-2020-0122.
J. P. Rowe, and J. C. Lester, “Artificial Intelligence for Personalized Preventive Adolescent Healthcare,” J. Adolescent Health, vol. 67, no. 2, pp. S52–S58, Aug. 01, 2020. doi: 10.1016/j.jadohealth.2020.02.021.