Novel machine learning technique for predicting teaching strategy effectiveness

Natalia Kushik, Nina Yevtushenko and Tatiana Evtushenko

International Journal of Information Management, 2020, vol. 53, issue C

Abstract: 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.

Keywords: Educational management; Level of proficiency; Evaluation/estimation/prediction; Logic network/circuit; Teaching strategy (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:53:y:2020:i:c:s0268401216000104

DOI: 10.1016/j.ijinfomgt.2016.02.006

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