Random forest application on cognitive level classification of E-learning content

Thomas, Benny; Chandra, Jagdish

doi:10.11591/ijece.v10i4.pp4372-4380

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…They used midterm and final exam questions from the Department of Information Systems as the dataset, categorized the learning objectives, and then provided exam questions for evaluating students. In Thomas and Chandra [4] purposed the framework which recommends appropriate learning content to a user of an e-learning system. This framework also focused on the cognitive level and separated learning content into three different levels: 6389 beginner level, which consists of remembering and understanding; intermediate level, which consists of applying and analyzing; and advanced level, which consists of evaluating and creating.…”

Section: Cognitive Levelsmentioning

confidence: 99%

“…We defined cognitive levels into three classes as used in [4] and defined the ICT skill levels. A beginner level blog is one in which the author includes basic knowledge about an ICT skill that the reader can understand without any previous knowledge.…”

Section: Skills Taggingmentioning

confidence: 99%

“…Knowledge units can be used as a guideline to build  ISSN: 2088-8708 models for recommending suitable content [2], although the knowledge domain limits their usefulness. Recently, machine learning algorithms have been applied to classify e-learning content into various cognitive levels [3], [4] and for text analysis [5]. Most of the current work is focused on English learning content.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive level classification on information communication technology skills for blog

Chootong

Charoensuk

2022

IJECE

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<span lang="EN-US">Learners can study and update their knowledge continually due to the rapid growth of online content. The Medium blog is a well-known open platform that encourages authors who want to share their experiences to publish content on various topics in multiple languages. Meanwhile, readers can query interesting content by searching for a related topic. However, finding suitable content is still challenging for learners, especially information communication technology (ICT) content in Thai, and needs to be classified into beginner, intermediate, and advanced cognitive levels. Moreover, ICT blog content is usually a mix of Thai language and technical terms in English. To overcome the challenge of content classification, a deep neural network (DNN) classification model was constructed to classify the ICT content from the Medium blog into three levels based on cognition. We examined and compared the classification results with strong baseline models, including logistic regression, multinomial naïve bayes, support vector machine (SVM), and multilayer perceptron (MLP). The experimental results indicate that the proposed DNN model attained the highest accuracy (0.878), precision (0.882), recall (0.878), and F1-score (0.875). </span>

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Section: Cognitive Levelsmentioning

confidence: 99%

Section: Skills Taggingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive level classification on information communication technology skills for blog

Chootong

Charoensuk

2022

IJECE

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“…Random Forest has also been successfully used to predict rainfall in anticipation of flooding with an accuracy of 99.45% [12]. In other studies that have been conducted by Thomas and Chandra on cognitive level classification, the use of Random Forest is able to provide high recommendations for elearning content based on the level of difficulty [13]. Even in other studies, Random Forest was able to provide 100% accuracy for predicting schizophrenia, even though the training data used was only 40% of the total data [14].…”

Section: Introductionmentioning

confidence: 99%

Jatropha Curcas Disease Identification using Random Forest

Saragih

Wijayaningrum

Haekal

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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As one of the most versatile plants, Jatropha curcas is spread in various regions around the world because of the great benefits it provides. However, Jatropha curcas is easily attacked by viruses which then cause damage to the plant, such as yellowing leaves and secreting sap, making it necessary to identify Jatropha curcas disease to deal with the problem as early as possible so that the losses incurred are not too large. An expert system was built to be able to identify Jatropha curcas disease by adopting expert knowledge. The use of the Random Forest algorithm as one of the classification algorithms was applied in this study. By using a random forest, several disease prediction classes are generated by each decision tree that has been formed. The disease class with the most votes was used as the final result. In this study, the data used were 166 data with 9 diseases and 30 symptoms. The results showed that Random Forest outperformed other algorithms such as Fuzzy Neural Network and Extreme Learning Machine with an accuracy of 98.002% using the composition of training data and test data of 70:30.

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“…It was first put forward by Professor Leo Breiman, an academician of the American Academy of Sciences in 2001. e basic classifier model of the random trees model is the classification regression tree model. In order to make the decision tree model in the random trees model different, two "random" processes are adopted in the process of building the random trees model [11] learning activity analysis is more and more widely used in education [12]. e traditional education data is single and one-sided, and cannot objectively present all the learning activities of learners.…”

Section: Introductionmentioning

confidence: 99%

Application of Random Trees Model in Online Learning Perspective in Evaluating Learners’ Behavioral Engagement

Wu¹,

Tang

2022

Mobile Information Systems

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Under digital technology, the vigorous development of online education has also encountered challenges of different degrees, such as the high dropout rate of learners, the low completion rate of courses, and the loss of users. Learning engagement has not yet formed an effective assessment system. Based on an exploration of the core of learning activity engagement, this research evaluates the state of learning activity engagement utilizing learners’ adaptive adjustment processes of information exchange activities and a random trees model. A combined classifier is a random tree. Random trees are a combined classifier. Its main idea is to build multiple relatively independent decision tree classifiers based on two random processes, and then obtain the final prediction results by voting all decision trees. The traditional random trees model is improved by weighted calculation and aggregation calculation. After experimental analysis, it can be found that the highest can reach more than 80%, which proves that the improvement of the weighted value has a good reflection on the random trees model, and the accuracy rate is increased by 65.2% after the weighted improvement. Overall, the performance of the improved random trees model is improved by 67.3%.

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Random forest application on cognitive level classification of E-learning content

Cited by 5 publications

References 20 publications

Cognitive level classification on information communication technology skills for blog

Cognitive level classification on information communication technology skills for blog

Jatropha Curcas Disease Identification using Random Forest

Application of Random Trees Model in Online Learning Perspective in Evaluating Learners’ Behavioral Engagement

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