Abstract
The development of Social and Emotional competences of students can significantly improve their learning and social outcomes. This prompts for tools to assist tutors in accomplishing social and emotional learning activities and evaluating the impact achieved. To do so, the blending of Recommender Systems with Machine Learning technologies can be proven beneficial for the design of intelligent and self-learning tools with the capacity to recommend activities, aligned with the social and emotional needs of educational groups. In the current manuscript, we detail a modeling approach for an interactive Recommender System that aims to suggest educational activities to tutors for improving the social and emotional competences of students, taking advantage of Reinforcement Learning techniques. A Reinforcement Learning model has been designed that considers the evolution of students’ social and emotional characteristics and the provided feedback through a set of interactions. Short evaluation of the detailed approach is provided, focusing on validating its appropriateness to serve educational needs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alkan, O., Daly, E.M., Botea, A.: An evaluation framework for interactive recommender systems. In: UMAP 2019 Adjunct, New York, NY, USA, pp. 217–218 (2019)
Bisquerra Alzina, R., Pérez González, J.C., Garcia Navarro, E.: Inteligencia emotional en educaión. Editorial Síntesis (2015)
Feidakis, M.: A review of emotion-aware systems for e-learning in virtual environments. In: Caballé, S., Clarisó, R. (eds.) Formative Assessment, Learning Data Analytics and Gamification, pp. 217–242. Academic Press, Boston (2016)
Fotopoulou, E., Zafeiropoulos, A.: RL-driven interactive recommender based on socioemotional behavioural models (2020). https://gitlab.com/netmode/arrle
Fotopoulou, E., Zafeiropoulos, A., Alegre, A.: Improving social cohesion in educational environments based on a sociometric-oriented emotional intervention approach. Educ. Sci. 9(1) (2019). https://doi.org/10.3390/educsci9010024
Goleman, D.: Emotional intelligence: why it can matter more than IQ. Bantam (1996)
Ie, E., et al.: RecSim: a configurable simulation platform for recommender systems (2019)
Ie, E., et al.: SlateQ: a tractable decomposition for reinforcement learning with recommendation sets. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, China, pp. 2592–2599 (2019)
Ie, E., et al.: Reinforcement learning for slate-based recommender systems: a tractable decomposition and practical methodology (2019)
Isinkaye, F., Folajimi, Y., Ojokoh, B.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 133–142 (2002)
Khanal, S., Prasad, P., Alsadoon, A., Maag, A.: A systematic review: machine learning based recommendation systems for e-learning. Educ. Inf. Technol. 1–30 (2019). https://doi.org/10.1007/s10639-019-10063-9
Linares, L., et al.: Developing cognitive-social-emotional competencies to enhance academic learning. Psychol. Schools 4, 405–417 (2005)
Louviere, J.J., Hensher, D.A., Swait, J.D., Adamowicz, W.: Stated Choice Methods: Analysis and Applications. Cambridge University Press, Cambridge (2000)
Mayer, J.D., Caruso, D.R., Salovey, P.: The ability model of emotional intelligence: principles and updates. Emot. Rev. 8(4), 290–300 (2016)
Moreno, J.: Who shall survive? Foundations of sociometry, group psychotherapy and socio-drama, January 1953
Petrides, K.: Ability and Trait Emotional Intelligence. The Blackwell-Wiley Handbook of Individual Differences, pp. 656–678, March 2013
Rivera, A.C., Tapia-Leon, M., Lujan-Mora, S.: Recommendation systems in education: a systematic mapping study. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 937–947. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_89
Salovey, P., Sluyter, D.J.: Emotional Development and Emotional Intelligence: Educational Implications. Basic Books (1997)
Schonert-Reichl, K.A.: Advancements in the landscape of social and emotional learning and emerging topics on the horizon. Educ. Psychol. 54(3), 222–232 (2019). https://doi.org/10.1080/00461520.2019.1633925
Sokal, L., Katz, J.: Social emotional learning and inclusive education. The Oxford Encyclopedia of Education, January 2017
Acknowledgements
This research effort is supported by ICCS Research Award under Grant Number 65020603.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fotopoulou, E., Zafeiropoulos, A., Feidakis, M., Metafas, D., Papavassiliou, S. (2020). An Interactive Recommender System Based on Reinforcement Learning for Improving Emotional Competences in Educational Groups. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-49663-0_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49662-3
Online ISBN: 978-3-030-49663-0
eBook Packages: Computer ScienceComputer Science (R0)