Students' learning style detection using tree augmented naive Bayes

Li, Ling Xiao; Rahman, Siti Soraya Abdul

doi:10.1098/rsos.172108

Cited by 41 publications

(36 citation statements)

References 19 publications

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“…Another review article [ 8 ], authors state that most researchers use the students’ personality traits in adaptive learning environments as an input to the learning style identification techniques. Also, in [ 9 ], the authors employed a tree augmented naïve Bayes network with greater detection accuracy in comparison to the Bayesian network. Another research effort [ 10 ] suggests the employment of neural networks and fuzzy logic techniques for the learning style detection; as reported by the authors, this resulted in the upgrading of the effectiveness of e-learning and knowledge-management systems.…”

Section: Related Workmentioning

confidence: 99%

“…However, in the related scientific literature [ 9 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ], a lack of students’ motivation to use a questionnaire in order to determine their learning preferences is observed due to the following reasons. Firstly, students consider that filling out the questionnaire is a boring task requiring an additional load of work.…”

Section: Automatic Identification Of Learning Styles and Instructimentioning

confidence: 99%

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Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Troussas

Krouska

Sgouropoulou

et al. 2020

Entropy

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Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.

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Section: Related Workmentioning

confidence: 99%

Section: Automatic Identification Of Learning Styles and Instructimentioning

confidence: 99%

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Troussas

Krouska

Sgouropoulou

et al. 2020

Entropy

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show abstract

“…Relación estudianteeducador: Usan técnicas de minería de datos con el fin de analizar patrones entre el uso de la tecnología y las experiencias de aprendizaje de los estudiantes, determinando de esa forma la ruta de aprendizaje de cada docente (Howard et al, 2016). Otros autores proponen una herramienta que es capaz de relacionar a los estudiantes con supervisores teniendo en cuenta a la experiencia, carga máxima de trabajo e interés de este último con respecto a las preferencias del estudiante (Sanchezanguix et al, 2019) En los trabajos de autores como Pereira et al 2018 TIC para la educación: sistema adaptativo basado en mecanismos de aprendizaje automático para la apropiación de tecnologías en estudiantes de educación media Evaluación de técnicas, algoritmos y modelos: Li et al, (2018) evalúan el desempeño del árbol de Naïve Bayes con el fin de determinar el estilo de aprendizaje de los estudiantes. Por otra parte, Núñez-Valdez et al, (2018) realizan una evaluación de doce (12) algoritmos populares dentro del aprendizaje automático: árboles de clasificación y regresión, redes neuronales artificiales y algoritmos para determinar la similitud, con el fin de construir un sistema de recomendación de libros electrónicos concebido como una tarea de regresión.…”

Section: Revisión Literariaunclassified

TIC para la educación: sistema adaptativo basado en mecanismos de aprendizaje automático para la apropiación de tecnologías en estudiantes de educación media

Otero

Rivera

Alarcón

et al. 2019

Telos

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The research presents an approach based on data analytics and automatic learning mechanisms integrated into one of the most widely used digital learning platforms in the world (open EDX) as a contribution to the improvement of learning processes in middle school students in municipalities of Colombia. Methodologically, according to Creswell y Plano Clark (2007), and Clements et al. (2017) a system was created to recommend educational contents adapted to the individual characteristics of students considering the limitations in the use of technologies in educational institutions; we developed interviews with teachers and focus groups with students of 10º and 11º grade; as a result, a functional architecture proposal was generated that allows the generation of initial recommendations of contents managed according to the students' performance and the characteristics of the territory.

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“…There are also Gregorc's learning styles, Riding cognitive styles, and Myer-Briggs Type Indicator [4]. FSLSM is the most widely used learning style in the education system, which shows a high level of reliability, internal consistency, and validity [4]- [8]. This model defines student learning styles into four different dimensions (Active/Reflective, Sensitive/Intuitive, Visual/Verbal, Sequential/Global) based on student behavior patterns that use Elearning systems [6].…”

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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Students' learning style detection using tree augmented naive Bayes

Cited by 41 publications

References 19 publications

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

TIC para la educación: sistema adaptativo basado en mecanismos de aprendizaje automático para la apropiación de tecnologías en estudiantes de educación media

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

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