Personalized Learning Model based on Deep Learning Algorithm for Student Behaviour Analytic

Mansur, Andi Besse Firdausiah; Yusof, Norazah; Basori, Ahmad Hoirul

doi:10.1016/j.procs.2019.12.094

Cited by 18 publications

(6 citation statements)

References 3 publications

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“…Some research groups want to study the factors that support and change learners' behavior and instructional methods [3], [13], [31], [33]. Some research groups want to study and develop tools that support the efficiency of teaching and learning [35], [37], [39]. Some research groups want to apply modern technology to be used in teaching and learning activities [40], [41].…”

Section: Improving Academic Achievementmentioning

confidence: 99%

“…They study the behavior of learners occurring in online activities through a learning management system known as MOOC. Firdausiah Mansur's research [37] proposed a personalized learning model to find suitable learning methods based on a deep learning algorithm. Both of their results are impressive because they have applied machine learning applications, data mining tools, and advanced statistics which enabled them to discover some facts from the data.…”

Section: Educational Data Miningmentioning

confidence: 99%

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Clustering of Mindset towards Self-Regulated Learning of Undergraduate Students at the University of Phayao

Nuankaew¹

2020

Adv. sci. technol. eng. syst. j.

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The effects of Covid-19 severely affected the Thai higher education model. Therefore, there are three significant objectives in this research: (1) to cluster the mindsets and attitudes toward self-regulated learning styles of undergraduate students at the University of Phayao. (2) to construct a predictive model for recommending an appropriate student learning clusters. (3) to evaluate the predictive model that has been constructed. Samples collected a compilation of 472 student satisfaction with questionnaires from three schools, with seven disciplines at the University of Phayao, Thailand. Research tools consisted of statistical and machine learning techniques as follows: frequency, percentage, average, standard deviation, k-means clustering, decision tree techniques, cross-validation methods, confusion matrix performance, accuracy, precision, and recall measurement. Researcher found that the k-means model with the highest accuracy is the decision tree model that was classified into three clusters by dividing the model testing into the leave-one-out crossvalidation method with a depth of seven levels of the decision tree model and an accuracy of 98.73%. From the results and studies, it can be concluded that the developed model is effective and reasonable to be further developed as an application for further organizational development.

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Section: Improving Academic Achievementmentioning

confidence: 99%

Section: Educational Data Miningmentioning

confidence: 99%

Clustering of Mindset towards Self-Regulated Learning of Undergraduate Students at the University of Phayao

Nuankaew¹

2020

Adv. sci. technol. eng. syst. j.

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“…On the other hand, several machine learning based approaches were identified in the literature. [29] and [30] focused on artificial intelligence techniques (Deep learning) to be applied for the different stages of online learning in order to personalize the learners' paths, namely LSTM neural networks.…”

Section: New Technologies and Adaptive Learningmentioning

confidence: 99%

“…In the following way, authors in [31] suggested a learning style prediction approach for adaptive e-learning, relying on unsupervised learning (K-means), in order to group learners according to their styles, as well as the use of supervised learning (Naive Bayes), with the aim of predicting the learning style of new learners. And in order to personalize the learning path [32] proposed the application of supervised learning (decision tree).…”

Section: New Technologies and Adaptive Learningmentioning

confidence: 99%

Design of An Adaptive E-learning Model Based on Artificial Intelligence for Enhancing Online Teaching

Mouna

Lakrami

Labouidya

2023

Int. J. Emerg. Technol. Learn.

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Nowadays, teaching involves more and more the usage of digital tools to accompany face-to-face learning. Such a system is being referred to as hybrid teaching. These digital tools include mainly distance learning platforms based on LMS (Learning management systems). LMS help to create on-line digital spaces to store and organize teaching material while providing a pedagogical learning process to connect educational communities. Despite the many forms of content that can be implemented on these platforms, from traditional file-based to interactive and animated content, these systems remain both passive and generic. They lack the ability to adapt to the learner in terms of learning skills, preferences, languages, intellectual abilities, learning patterns and rhythms. This paper proposes a design and modeling of an intelligent and dynamic adaptive learning system based on artificial intelligence with the main objective of identifying and providing personalized learning environments adapted to the learner needs.

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“…7. Firdausiah, Yusofa & Hoirul (2019) proponen un modelo de aprendizaje personalizado basado en un algoritmo de aprendizaje profundo que realiza una analítico del comportamiento del estudiante logrando de esta manera la adaptabilidad. El modelo propuesto se integra por cuatro componentes: componente basado en competencias en donde se encuentran los objetos de aprendizaje; individualizado, en el que se tiene el modelo del aprendiz; diferenciado; y adaptativo.…”

Section: Figura 10 Técnicas De Inteligencia Artificial Empleadas En L...unclassified

Modelos Tecnológicos de Aprendizaje Adaptativo Aplicados a la Educación

Quintanar-Casillas

López

2022

RTED

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En los últimos diez años, se han publicado diversas investigaciones que proponen modelos o sistemas de aprendizaje adaptativo o personalizado tomando como base el estudio de los estilos de aprendizaje, las habilidades cognitivas o la interacción del estudiante con los objetos de aprendizaje. El objetivo de esta investigación fue efectuar una revisión sistemática de los modelos o sistemas de aprendizaje adaptativo propuestos durante el periodo 2012-2021 tomando en cuenta autores de origen hispanoamericano. Para su realización, se empleó el método analítico, bajo el paradigma positivista, con enfoque cuantitativo de tipo descriptivo apoyado en el metaanálisis y con un diseño experimental de orden transversal. Se aplicó la metodología de análisis de contenido apoyada en las directrices de la declaración Preferred Reporting Items for Systematic reviews and Meta-Analyses para la elaboración de revisiones sobre una muestra de 50 estudios seleccionados de diferentes bases de datos científicas que propusieran modelos o sistemas de aprendizaje adaptativo. Se utilizaron dos categorías de análisis: objeto de estudio y estructura del sistema o modelo propuesto. A través de una matriz bibliográfica, como, una matriz analítica se registró y organizó la información recabada. Dentro de los resultados obtenidos se observó que la mayor parte de los estudios tienen estructuras basadas en tres componentes, siendo la inteligencia artificial la técnica de adaptabilidad más empleada; de igual forma, se observó una relativa exigüidad de estudios elaborados en Hispanoamérica. Se concluyó mencionando los nichos de investigación sobre aprendizaje adaptativo aplicado a la Educación y una serie de sugerencias en torno a trabajos futuras.

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Personalized Learning Model based on Deep Learning Algorithm for Student Behaviour Analytic

Cited by 18 publications

References 3 publications

Clustering of Mindset towards Self-Regulated Learning of Undergraduate Students at the University of Phayao

Clustering of Mindset towards Self-Regulated Learning of Undergraduate Students at the University of Phayao

Design of An Adaptive E-learning Model Based on Artificial Intelligence for Enhancing Online Teaching

Modelos Tecnológicos de Aprendizaje Adaptativo Aplicados a la Educación

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