Abstract
In recent decades, learning methods have evolved, adapted, and reinvented. With these changes, the curriculum has become increasingly complex and there is an opportunity for technology to offer a helping hand in providing superior educational experiences. In this paper, we highlight the need for a recommender system within the Montessori kindergartens while exploring the main techniques of the recommender systems used in large environments such as YouTube, LinkedIn, or Amazon. Our ultimate goal is to obtain a recommender system—similar to an intelligent assistant—that helps teachers in planning learning paths and guides students in making progress.
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Marshall, C.: Montessori education: a review of the evidence base. npj Sci. Learn. 2(1), 1–9 (2017)
Jain, S., Grover, A., Thakur, P.S., Choudhary, S.K.: International Conference on Computing, Communication and Automation (ICCCA 2015)
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer, Berlin (2011)
Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M.: Basic Approaches in Recommendation Systems
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. In: Foundations and Trends in Human-Computer Interaction (2011)
Burke, R., Felfernig, A., Goeker, M.: Recommender systems: an overview. AI Mag. 32(3), 13–18 (2011)
Item-Based Collaborative Filtering Recommendation Algorithms reading report. https://haelchan.me/2017/11/03/Item-Based-CFRA-reading-report/. Last accessed 15 Jan 2020
Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)
Chen, Y., Li, X., Liu, J., Ying, Z.: Recommendation system for adaptive learning. Appl. Psychol. Measur. 42(1), 24–41 (2018)
Adaptive Learning in the Classroom and Beyond. https://edtechnology.co.uk/Blog/adaptive-learning-in-the-classroom-and-beyond/. Last accessed 24 Jan 2020
Khorasani, E.S., Zhenge, Z., Champaign, J.: A Markov Chain Collaborative Filtering Model for Course Enrollment Recommendations (2016)
Acknowledgements
This work would not have been possible without the constant feedback and help in understanding the needs of a Montessori system given by Mr. Adrian Nache and Mr. Dan Tarko. We are also truly grateful to Andrei Stanila for his help in developing the Montessmile platform.
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Nica, C., Olteanu, A., Racec, E. (2021). Toward a Recommender System for Planning Montessori Educational Activities. In: Mealha, Ó., Rehm, M., Rebedea, T. (eds) Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Smart Innovation, Systems and Technologies, vol 197. Springer, Singapore. https://doi.org/10.1007/978-981-15-7383-5_14
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DOI: https://doi.org/10.1007/978-981-15-7383-5_14
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