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Cited by 31Intelligent Tutoring Systems and Adaptive LearningOnline Learning and AnalyticsLearning Styles and Cognitive Differences

Modelling an Adaptive e-Learning System Using LSTM and Random Forest Classification

Anantharaman, Harish, Mubarak, Abdullah, Shobana, B.T·2018·2018 IEEE Conference on e-Learning, e-Management and e-Services (IC3e)
DOI 10.1109/ic3e.2018.8632646
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Abstract

E-learning is the most sought after mode of self-learning amongst students and learners today. There is an undisputed requirement for dynamism and customized learning materials that augment a student's understanding of the subject. This paper proposes a comprehensive model based on deep learning by enabling the use of Long Short-Term Memory(LSTM) and Random Forest classification, thereby providing a highly personalized e-Learning system. The adaptive learning system would make use of a bidirected graph data structure that would comprise of various levels and phases. The structure and traversal of the graph are determined by the appropriate machine learning and deep learning algorithms. Convolutional Neural Networks(CNN) and Deep learning algorithms such as Long Short-Term Memory(LSTM) help in the identification of the learner style in accordance to the Felder and Silverman learning style modelling(FSLSM).Once the learner style has been determined, the model uses a Random Forest classifier that takes in a number of parameters including the assessment details of the students to return a prediction of the learner level which pertains to the difficulty of the course. The learner style and level, together would determine the adaptivity of the course. In such a mechanism, the e-Learning course would be adapted with respect to the abilities of the learner, thus allowing a superlative understanding of the course.

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  • In [31], LSTM, random forest classification, and CNNs were used to create a deep learning model for a personalized e-learning system, with random forest predicting learner level and CNN identifying learner style.

    “The machine learning algorithms used in the article are long short-term memory (LSTM), random forest classification, and convolutional neural networks (CNNs).The method employed is the creation of a comprehensive model based on deep learning to provide a highly personalized e-learning system.”

    From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [31]

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Related

Authors
Anantharaman, H., Mubarak, A., & Shobana, B. T.
Venue
2018 IEEE Conference on e-Learning, e-Management and e-Services (IC3e)
Publisher
IEEE
Published
2018-11
Pages
29-34
DOI
10.1109/ic3e.2018.8632646
OpenAlex
W2913498829
Cited by
30
References
27
Citations by year
2026: 1 · 2025: 1 · 2024: 8 · 2023: 6 · 2022: 9 · 2021: 3 · 2020: 1 · 2019: 1
FWCI
0.51
Type
conference paper
Language
en
Open access
closed
Locations
IEEE Conference on e-Learning, e-Management and e-Services (IC3e) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
Keywords
adaptive e-learning, personalized learning, Felder-Silverman learning styles, LSTM, random forest classification, convolutional neural networks

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