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.

In recent years, E-learning has transformed teaching and learning pedagogy. Unlike traditional teaching, E-learning is more of student-centred learning and is based on learning activities. Researchers have evolved with the most suitable e-learning activities to consider while designing any virtual course. However, adapting to online activities and achieving the desired learning outcome varies from learner to learner. It is generally observed that the learners attain the learning outcome much faster if guided with their preferred learning activities. Thus, identifying the most preferred learning activities of a learner will ensure quicker learning capacities. The existing methods tend to streamline the learners into visual, auditory, and kinaesthetic types, however, the learning activities of a learner are not prioritized, which is addressed in this paper. If, the set of learning activities is represented in n dimensions, reducing the n dimensions to a fewer dimension is required. Out of the existing dimensionality reduction methods, principal component analysis (PCA) best reduces the dimensions while preserving the integrity of the data set. PCA achieves this by computing principal components which are uncorrelated variables maximizing the variance. In this paper, we propose an algorithm to identify one of the most preferred learning activities of learners through the application of the PCA method, and then the prioritization of the learning activities will be compared with the Pearson correlation co-efficient method for the accurateness of the suggested algorithm.

The K-means algorithm is the most widely used partitional clustering algorithms. In spite of several advances in K-means clustering algorithm, it suffers in some drawbacks like initial cluster centres, stuck in local optima etc. The initial guessing of cluster centres lead to the bad clustering results in K-means and this is one of the major drawbacks of K-means algorithm. In this paper, a new strategy is proposed where we have blended K-means algorithm with genetic algorithm (GA) and volume metric algorithm (VMA) to predict the best value of initial cluster centres, which is not in the case of only K-means algorithm. The paper concludes with the analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different well-known datasets from UCI machine learning repository.

The process of clustering in the general perspective is limited to the grouping of data into clusters and finds its applications in the fields of information retrieval, text ranking and classification and more. The dimension of e-Learning is to improve learning with various tools and technologies. Grouping of learners based on their learning levels is found to improve the learning abilities. Scientific method to cluster the learners is not available in literature, which can further simplify the amalgamation of learning complemented through clustering. This paper is an attempt to examine the aspects of implementing clustering to group the learners according to their learning abilities.