Novel machine learning technique for predicting teaching strategy effectiveness

Autor: Natalia Kushik, Tatiana G. Evtushenko, Nina Yevtushenko
Přispěvatelé: Département Réseaux et Services Multimédia Mobiles (RS2M), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Méthodes et modèles pour les réseaux (METHODES-SAMOVAR), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Institut Polytechnique de Paris (IP Paris), Tomsk State University [Tomsk]
Rok vydání: 2020
Předmět:
Evaluation/estimation/prediction
Logic network/circuit
Computer Networks and Communications
Process (engineering)
Computer science
media_common.quotation_subject
Foreign language
02 engineering and technology
Library and Information Sciences
Machine learning
computer.software_genre
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Order (exchange)
020204 information systems
0502 economics and business
ComputingMilieux_COMPUTERSANDEDUCATION
0202 electrical engineering
electronic engineering
information engineering
Level of proficiency
Quality (business)
media_common
business.industry
4. Education
05 social sciences
Information technology
Logic gate
Scalability
050211 marketing
Educational management
Teaching strategy
Artificial intelligence
business
computer
Information Systems
Zdroj: International Journal of Information Management
International Journal of Information Management, Elsevier, 2020, 53, pp.101488:1-101488:10. ⟨10.1016/j.ijinfomgt.2016.02.006⟩
ISSN: 0268-4012
0143-6236
DOI: 10.1016/j.ijinfomgt.2016.02.006
Popis: International audience; In this paper, we present an approach for evaluating and predicting the student’s level of proficiency when using a certain teaching strategy. This problem remains a hot topic, especially nowadays when information technologies are highly integrated into the educational process. Such a problem is essential for those institutions that rely on e-learning strategies as various techniques for the same teaching activities and disciplines are now available online. In order to effectively predict the quality of this type of (electronic) educational process we suggest to use one of the well known machine learning techniques. In particular, a proposed approach relies on using logic circuits/networks for such prediction. Given an electronic service providing a teaching strategy, the mathematical model of logic circuits is used for evaluating the student’s level of proficiency. Given two (or more) logic circuits that predict the student’s educational proficiency using different electronic services (teaching strategies), we also propose a method for synthesizing the resulting logic circuit that predicts the effectiveness of the teaching process when two given strategies are combined. The proposed technique can be effectively used in the educational management when the best (online) teaching strategy should be chosen based on student’s goals, individual features, needs and preferences. As an example of the technique proposed in the paper, we consider an educational process of teaching foreign languages at one of Russian universities. Preliminary experimental results demonstrate the expected scalability and applicability of the proposed approach.
Databáze: OpenAIRE
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