Title: Evaluation of e-learning as a learning agent

Authors: Gede Rasben Dantes; Ni Ketut Suarni; I. Ketut Dharsana; Nyoman Dantes; I. Nyoman Laba Jayanta; Ni Komang Arie Suwastini; Gede Jana Adi Putra

Addresses: Information Management Department, Universitas Pendidikan Ganesha, Udayana Street, No. 11, Singaraja, Bali, Indonesia ' Guidance and Counselling Department, Universitas Pendidikan Ganesha, Udayana Street, No. 11, Singaraja, Bali, Indonesia ' Guidance and Counselling Department, Universitas Pendidikan Ganesha, Udayana Street, No. 11, Singaraja, Bali, Indonesia ' Research and Education Evaluation Department, Universitas Pendidikan Ganesha, Udayana Street, No. 11, Singaraja Bali, Indonesia ' Elementary Education Department, Universitas Pendidikan Ganesha, Udayana Street, No. 11, Singaraja, Bali, Indonesia ' English Education Department, Universitas Pendidikan Ganesha, Ahmad Yani Street, No. 67, Singaraja, Bali, Indonesia ' SMA Negeri 1 Kuta, Dewi Saraswati Street, Kuta, Badung, Bali, Indonesia

Abstract: The aim of this study was to evaluate the e-learning as a learning agent. The main feature of this system is to identify a learning style of the student through a log system that relates the students' achievement in mastering the learning content. The students' learning style was determined by Bayesian network algorithm that enabled the system to classify the students' learning style into textual, audio, or video. With this identification, the system would be able to recommend suitable learning contents for the students. The study revealed that the e-learning was capable of identifying the students' learning style. From the total 34 research subjects involved in the study, the system successfully identified 16 students' learning styles, where 14 students were revealed to have textual learning style, one student was revealed to prefer audio content, and the other student was revealed to prefer video content. Thus, the study concluded that the adaptive e-learning system evaluated in the study was capable of identifying the students' leaning styles, and thus capable of recommending suitable materials for the students, hence rendering the e-learning system as a mere content management system, but also as an agent of learning.

Keywords: e-learning as a learning agent; e-learning; learning style; Bayesian network; BN.

DOI: 10.1504/IJIL.2019.099989

International Journal of Innovation and Learning, 2019 Vol.25 No.4, pp.451 - 464

Received: 02 Dec 2017
Accepted: 03 Sep 2018

Published online: 07 May 2019 *

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