How Does Learners' Prefer to Process Information in MOOCs? A Data-driven Study

Hmedna, Brahim; Mezouary, Ali El; Baz, Omar

doi:10.1016/j.procs.2019.01.045

Cited by 17 publications

(7 citation statements)

References 13 publications

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“…This research is used to predict the learners' engagement in MOOCs by choosing optimal clusters. Brahim Hmedna et al [7] proposed a method, how does a learner prefer to process information in MOOCS? Using the K-Means clustering algorithm, this study discovered that the majority of learners favour active learning styles.…”

Section: Related Workmentioning

confidence: 99%

Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid

Raj¹,

Vidyaathulasiraman²

2021

IJACSA

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Learning is appropriate when the learners are grouped and facilitated to learn according to their learning style and at their own pace. Elaborate researches have been proposed to categorize learners based on various e-learning parameters. Most of these researches have deployed the clustering principles for grouping eLearners, and in particular, they have utilized K-Medoid principle for better clustering. In the classical K-Medoid algorithm, predicting or determining the value of K is critical, two methods namely the Elbow and Silhouette methods are widely applied. In this paper, we experiment with the application of both these methods to determine the value of K for clustering eLearners in K-Medoid and prove that Silhouette method best predicts the value of K.

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Section: Related Workmentioning

confidence: 99%

Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid

Raj¹,

Vidyaathulasiraman²

2021

IJACSA

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“…Hmedna et al [20], they extracted features describing FSLSM learning style in order to construct a vector of characteristics for each learner. Then apply k-means clustering to partition learners into different groups according to their learning style preferences.…”

Section: Machine Learningmentioning

confidence: 99%

Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

Hosny

El-Korany

2022

IJECE

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<p>Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.</p>

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“…It proposes a series of variables to include in the learning process as factors causing differences between learners. These variables are divided into five categories: affective, sociological, environmental, psychological and physiological [14].…”

Section: Learning Styles Modelmentioning

confidence: 99%

“…The collaborative approach is founded on the gathering of data from the survey, and provides a static learner profile model [14], [48]. The learner model is set up only once and remains unchanged throughout the learning process, which goes against the findings of researchers who believed that learning styles are rather unstable.…”

Section: Approaches To Identifying Learning Stylesmentioning

confidence: 99%

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Benabbes¹,

Hmedna²,

Housni³

et al. 2022

Int. J. Interact. Mob. Technol.

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Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identify learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As missing good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style.

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How Does Learners' Prefer to Process Information in MOOCs? A Data-driven Study

Cited by 17 publications

References 13 publications

Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid

Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid

Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

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