Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach

Aissaoui, Ouafae El; Alami, Yasser El Madani El; Oughdir, Lahcen; Allioui, Youssouf El

doi:10.1109/isacv.2018.8354021

Cited by 24 publications

(18 citation statements)

References 8 publications

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“…Ouafae et al [13] suggested an automated approach to identifying student learning styles based on web mining on moodle platforms. The process of classifying student log files corresponds to Felder and Silverman's learning style models.…”

Section: Related Workmentioning

confidence: 99%

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Ikawati

Rasyid

Winarno

2021

emitter

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Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model.

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Section: Related Workmentioning

confidence: 99%

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Ikawati

Rasyid

Winarno

2021

emitter

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“…Learning styles can also be detected based on web usage mining techniques [14]. Instead of asking students to fill out a questionnaire on their learning style, Ouafae El Aissaoui et.al detects students's learning style based on web usage mining.…”

Section: Related Workmentioning

confidence: 99%

Develop a User Behavior Analysis Tool in ETHOL Learning Management System

Susanto

Qurani

Rasyid

2021

emitter

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Students have different learning styles when studying online. Meanwhile, lecturers use the same method for all students who take their online lectures. These different learning styles can affect the level of understanding and the results obtained by students. By knowing student learning styles, lecturers are expected to be able to use the right way in delivering material. In this research, we developed a student behavior analysis feature on self-developed Virtual Learning Environment (VLE) called Enterprise Hybrid Online Learning (ETHOL). Students’ data collected includes data on online activities, personal data, and survey data on student learning styles. User behavior analysis was carried out by dividing into three clusters: average scores, time to collect assignments, and student learning styles. The clustering method used is the Hierarchical K-Means. The results obtained are students who have the habit of collecting assignments on time have higher scores than others. In addition, the lecturer is able to see the results of the analysis of the behavior and learning styles of each student. These results can be used as information in delivering lecture material.

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“…As reported by other studies [ 12 , 13 ], the learners’ skills and their prior knowledge are the key characteristics that have been used towards the automatic detection of learning styles; both research efforts focused on mapping students’ skills in terms of knowledge of facts and meaning and integration of and application of knowledge being closely related to learning style. In [ 14 ], the authors present an automatic approach for detecting students’ learning style based on web usage mining; specifically, the students’ log files were classified using clustering algorithms with a view to detect their learning style. In [ 15 ], the authors explored four computational intelligence techniques (artificial neural network, ant colony optimization, genetic algorithm, and particle swarm optimization) to improve the accuracy of learning style detection by employing the FSLSM and the students’ behavior.…”

Section: Related Workmentioning

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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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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Integrating web usage mining for an automatic learner profile detection: A learning styles-based approach

Cited by 24 publications

References 8 publications

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Develop a User Behavior Analysis Tool in ETHOL Learning Management System

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

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