Recommender system for ubiquitous learning based on decision tree

Guabassi, Inssaf El; Achhab, Mohammed Al; Jellouli, Ismail; Mohajir, Badr Eddine El

doi:10.1109/cist.2016.7805107

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…For instance, if learners with a similar profile rated a course in various contexts, it will not be recommended to them because of the contextual difference. Therefore, a context-based recommendation can satisfy the real and effective expectations of the learner [20]. A contextual learning system is any learning environment that allows learners to access learning content from anywhere at any time, whether mobile devices or wireless communications are utilized or not.…”

Section: Learning Recommendation Contextmentioning

confidence: 99%

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

Benabbes

Housni

Zellou

et al. 2023

Int. J. Emerg. Technol. Learn.

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The learning management system (LMS) is an e-learning software that raised the interest of disparate learners’ groups. However, learners have difficulties in finding learning resources tailored to their preferences in the best way at the right time. Making the learning process more efficient and pleasant for learners can be achieved by using context and learning styles such as customizing aspects. This study proposes a new data-driven approach to retrieve learners' characteristics using traces of their activities based on the Felder-Silverman Learning Style Model (FSLSM). In this research, the traces of 714 learners who enrolled in three agronomy courses taught at IAV HASSAN II (winter session 2019, 2020, and 2021) were analyzed. Learners are categorized into clusters by their preference level for global/sequential learning styles, using an unsupervised clustering method. Then a classifier model tailored to our requirements was trained and based on the learner's learning style and their current context, a learning object recommendation list is proposed for them. The results revealed that the k-means algorithm performed well in identifying learning styles (LS) and the use of context features defined from the learners' adaptive close environments improved learning performance with an accuracy of over 96% given that most of the learners preferred a global learning style.

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Section: Learning Recommendation Contextmentioning

confidence: 99%

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

Benabbes

Housni

Zellou

et al. 2023

Int. J. Emerg. Technol. Learn.

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“…Kemudian hitung masing-masing entropy dari semua nilai variabel yang ada. Dan dengan menggunakan (2) dapat menghitung nilai information gain tiap-tiap atribut [18,19,20].…”

Section: Implementasi Algoritmaunclassified

Rekomendasi Pemilihan Restoran Berdasarkan Rating Online Menggunakan Algoritma C4.5

Farid

Fitrianah

2021

InComTech

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Restoran adalah suatu usaha yang menyediakan tempat untuk menikmati hidangan kepada pelanggan serta menetapkan tarif tertentu. Tersedianya banyak pilihan restoran menjadi faktor penting yang dibutuhkan dalam memilih restoran. Masalah akan muncul secara langsung akibat banyaknya restoran yang tersedia sehingga membutuhkan waktu yang lama untuk menentukan pilihan. Hal ini disebabkan oleh penyebaran informasi yang tidak merata dan pengambilan keputusan yang tidak akurat sehingga pelanggan kesulitan untuk menentukan pilihan restoran. Dengan adanya rekomendasi pemilihan restoran untuk pelanggan akan mendukung pengambilan keputusan dalam menentukan restoran atau tempat makan. Latar belakang dipilihnya algoritma C4.5 sebagai pengambilan keputusan untuk menentukan rekomendasi pemilihan restoran berdasarkan rating. Data yang digunakan diambil dari Zomato API untuk dilakukan pengujian dengan menggunakan 1003 sampel data restoran. Hasil yang didapatkan dengan ten-fold cross validation yaitu menghasilkan akurasi 86,24% dengan rating yang paling dominan dan sesuai untuk direkomendasi adalah Rating Good. Hal ini diharapkan dapat memberikan rekomendasi untuk menentukan beberapa pilihan restoran yang sesuai untuk dikunjungi berdasarkan rating tersebut.

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“…This paper represents a continuation of our previous works carried out in the adaptive learning systems (ALS) [1][2][3][4], in which we provided adaptivity in ubiquitous learning systems based on learning styles of Felder-Silverman and learner context. However, a new adaptation problem has appeared, namely the automatic detection of learner learning styles.…”

Section: Introductionmentioning

confidence: 99%

Identifying learning style through eye tracking technology in adaptive learning systems

Guabassi

Bousalem

Achhab

et al. 2019

IJECE

Self Cite

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Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.

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Recommender system for ubiquitous learning based on decision tree

Cited by 12 publications

References 6 publications

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

Rekomendasi Pemilihan Restoran Berdasarkan Rating Online Menggunakan Algoritma C4.5

Identifying learning style through eye tracking technology in adaptive learning systems

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