Concept-based and Fuzzy Adaptive E-learning

Duhayyim, Mesfer Al; Newbury, Paul

doi:10.1145/3234825.3234832

Cited by 9 publications

(4 citation statements)

References 24 publications

(29 reference statements)

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“…One such study [31] used decision trees (DT) to help students make learning better in an e-learning setting. Similarly, naive Bayes, fuzzy logic [32], and fuzzy inductive systems and reasoning [33] were also used to construct classification and prediction models.…”

Section: Related Workmentioning

confidence: 99%

An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

et al. 2022

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Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.

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Section: Related Workmentioning

confidence: 99%

An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

et al. 2022

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“…Pre-test ile elde edilen kavram haritasına göre bulanık sistem ile öğrenci öğrenme yapısı oluşturulmaktadır. Kavram haritası her bir öğrenci için öğrenme düzeylerini göstermektedir (Al Duhayyim, 2019).…”

Section: öğRenci Performansı Değerlendirmeunclassified

Uzaktan Eğitimde Kullanılan Bulanık Mantık Tabanlı Öğrenme Modelleri, Platformlar, Ölçme ve Değerlendirme Yöntemleri

ÖZSEVEN>¹,

Çağman²

2021

European Journal of Science and Technology

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ÖzUzaktan eğitim, geleneksel eğitimin teknolojik araçlar yardımıyla zaman ve mekan bağımsız olarak gerçekleştirilmesidir. Teknolojik gelişmeler ve pandemi sürecinin başlaması ile birlikte uzaktan eğitime olan talep çok yüksek seviyelere ulaşmıştır. Uzaktan eğitim sürecinin başarı ile idame ettirilmesi ve öğrencinin eğitim hayatını başarılı bir şekilde sürdürülmesi için kullanılacak öğrenme modelleri, platformlar ve ölçme değerlendirme yöntemleri önem arz etmektedir. Bu çalışma, uzaktan eğitimde kullanılan öğrenme modelleri, platformlar, ölçme ve değerlendirme ve uzaktan eğitimde bulanık mantığın kullanımına ilişkin bir derleme çalışmasıdır. Araştırma kapsamında uzaktan eğitimin uygulanmasına ilişkin literatür taraması yapılmıştır. Araştırma sonucunda, senkron ve asenkron sistemleri destekleyen platformların daha etkin eğitim sağladığı, öğrencilerin sistemi kullanım desenlerinin de ön planda olduğu, akademik güvensizliğin önüne geçmek için yapay zeka tekniklerinden yararlanıldığı ortaya konulmuştur. Ayrıca, bulanık mantığın öğrenme desenlerinin belirlenmesi, platform seçimi ve ölçme ve değerlendirme de yaygın olarak kullanıldığı sonucuna ulaşılmıştır.

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“…In this context, concept maps represent another big advantage. In [23], a new technique is proposed to construct them automatically. It was then extended to multiple adaptive education systems.…”

Section: Fuzzy Logic In Educational Systemmentioning

confidence: 99%

Converting cumulative grade point average to an equivalent percentage value based on a fuzzy logic

Fattoh

Mousa²,

Safwat³

2021

IJEECS

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The graduates who have finished their study program will be given a merit award and their award certificates will be graded in accordance with the degree of their academic accomplishment. The awards are generally offered using two methods; one is by the cumulative grade point average (CGPA) and the other is by the average percentage of all marks for the students. The problem is when assigning a course final grade; each student's final percentage is translated to a letter, allowing the discrepancy within the same letter grade range in the final ranking. If two students have the same final score, that means equal results. However, this equality can be false if one student hits a percentage of the highest grade, while the second student earns a percentage of the lowest grade of the same letter grade. This paper introduced a new equation that transforms between the awarded cumulative grade point average and the awarded percentage ranking based on fuzzy system. The proposed approach was tested using three actual benchmarks collected from three different colleges in Beni-Suef university. The obtained results reflects the effect of the fuzzy logic in helping converting form CGPA measures to percentage measure in educational systems.

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Concept-based and Fuzzy Adaptive E-learning

Cited by 9 publications

References 24 publications

An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance

Uzaktan Eğitimde Kullanılan Bulanık Mantık Tabanlı Öğrenme Modelleri, Platformlar, Ölçme ve Değerlendirme Yöntemleri

Converting cumulative grade point average to an equivalent percentage value based on a fuzzy logic

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