Using Automatic Detection to Identify Students' Learning Style in Online Learning Environment -- Meta Analysis

Ahmad, Norazlina; Tasir, Zaidatun; Shukor, Nurbiha A.

doi:10.1109/icalt.2014.45

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The literature-based method uses student behavior and actions with the system to identify their learning preferences. Some studies that use a literature-based approach include [2], [15], [17], [27], [28]. The developed method uses simple rule-based methods to calculate learning styles from the number of suitable instructions and does not involve system design.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Performance of the Automatic Learning Style Detection Model using a Combination of Modified K-Means Algorithm and Naive Bayesian

Hidayat¹,

Wardoyo²,

Sn³

et al. 2020

IJACSA

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Learning Management System (LMS) is well designed and operated by an exceptional teaching team, but LMS does not consider the needs and characteristics of each student's learning style. The LMS has not yet provided a feature to detect student diversity, but LMS has a track record of student learning activities known as log files. This study proposes a detection model of student's learning styles by utilizing information on log file data consisting of four processes. The first process is pre-processing to get 29 features that are used as the input in the clustering process. The second process is clustering using a modified K-Means algorithm to get a label from each test data set before the classification process is carried out. The third process is detecting learning styles from each data set using the Naive Bayesian classification algorithm, and finally, the analysis of the performance of the proposed model. The test results using the validity value of the Davies-Bouldin Index (DBI) matrix indicate that the modified K-Means algorithm achieved 2.54 DBI, higher than that of original K-Means with 2.39 DBI. Besides having high validity, it also makes the algorithm more stable than the original K-Means algorithm because the labels of each dataset do not change. The improved performance of the clustering algorithm also increases the values of precision, recall, and accuracy of the automatic learning style detection model proposed in this study. The average precision value rises from 65.42% to 71.09%, the value of recall increases from 72.09% to 80.23%, and the value of accuracy increases from 67.06% to 71.60%.

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