A comprehensive adaptive system for e-learning of foreign languages

Bradáč, Vladimír; Walek, Bogdan

doi:10.1016/j.eswa.2017.08.019

Cited by 28 publications

(14 citation statements)

References 12 publications

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“…Some of the data mining techniques deployed includes clustering, text mining, association rule mining, classification and so forth. The mode of delivery of education has transcended from the traditional classroom-based method to various web-based teaching platforms and adaptive e-learning systems [19] due to the availability of modern learning technologies [20][21][22], and this therefore enables diverse education related data to be generated, logged, processed and studied for a better understanding of the learning process and learners [23], and educational technology integration practices [24].…”

Section: Introductionmentioning

confidence: 99%

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Adekitan

Salau

2019

SN Appl. Sci.

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The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions toward preventing failure and dropout tendencies. Students are generally admitted based on their evaluated academic potentials as measured using their admission criteria scores. This study seeks to identify the relationship, if any, between the admission criteria scores and the graduation grades, and to examine the influence of ethnicity using the geopolitical zone of origin of the student on the predictive accuracy of the models developed using a Nigerian University as a case study. Data mining analyses were carried out using four classifiers on the Orange Software, and the results were verified with multiple regression analysis. The maximum classification accuracy observed is 53.2% which indicates that the pre-admission scores alone are insufficient for predicting the graduation result of students but it may serve as a useful guide. By applying over-sampling technique, the accuracy increased to 79.8%. The results establish that the ethnic background of the student is statistically insignificant in predicting their graduation results. Hence, the use of ethnicity in admission processes is therefore not ideal.

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Section: Introductionmentioning

confidence: 99%

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Adekitan

Salau

2019

SN Appl. Sci.

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“…The feasibility of using pedagogical capabilities of e-learning in the digital educational space is considered during in different studies (Gottlieb, 2016;Gushchina & Ochepovsky, 2017;Snegireva, 2017;Sorochinsky, 2018;Vaganova et al, 2017). Analysis of studies and publications, which note that the readiness of students and teachers to implement e-learning in the digital educational environment of the university continues to be insufficient (Bradac & Walek, 2017;Skoulikari et al, 2015;Schulz et al, 2014). Various approaches to solving the problem of teacher training in the context of e-learning by means of the digital educational environment of the university are considered in different studies (Akupiyan, 2019;Baraskhanova & Danilova, 2018;Dyakova & Sechkareva, 2019;Komova, Musatova, 2019;Vezirov & Babayan, 2015).…”

Section: Research Questionsmentioning

confidence: 99%

E-Learning Implementation By Means Of The Digital Educational Environment Of The University

Везиров¹

2021

Knowledge, Man and Civilization - Proceedings of International Scientific Congress «Knowledge, Man and Civilization» (ISCKMC 20

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“…3: For the existing channels, find the nodes that satisfy the resonance condition as in (3), in order from the highest activation value to the lowest value. 4: if There is no node that satisfies the resonance condition. then…”

Section: B Learner Status Dlnmentioning

confidence: 99%

“…. , c, a} by using (4). J is the index of the node that satisfies the resonance condition for all channels in Step 7.…”

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confidence: 99%

Individualized AI Tutor Based on Developmental Learning Networks

Kim

2020

IEEE Access

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In recent years, in the field of education technology, artificial intelligence tutors have come to be expected to provide individualized educational services to help learners achieve high levels of academic success. To this end, AI tutors need to be able to understand the current status and preferences of a learner and then suggest appropriate learning contents accordingly. However, it is challenging to monitor learner status and preferences continually and to recommend appropriate educational services. In this paper, we propose an individualized AI tutor as an integrated system of three developmental learning networks (DLNs) by extending a deep adaptive resonance theory (Deep ART) network, a neural network capable of incremental learning. Specifically, the learner status DLN is able to easily add new input channels about learner status without disrupting existing classifiers. The learner preference DLN is to categorize learner preferences based on frequency as well as sequence of events. The learner experience DLN is updated to immediately reflect alteration of the educational effectiveness in the current classification. Our AI tutor is currently embedded in a commercialized mobile application for teaching the Korean language to children. Experimental results show that the AI tutor application efficiently helps children learn the Korean language.

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A comprehensive adaptive system for e-learning of foreign languages

Cited by 28 publications

References 12 publications

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance

E-Learning Implementation By Means Of The Digital Educational Environment Of The University

Individualized AI Tutor Based on Developmental Learning Networks

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