Clustering of online learning resources via minimum spanning tree. Issue 2 (5th September 2016)

• Record Type: Journal Article
• Authors: Wu, Qingyuan; Zhan, Changchen; Wang, Fu Lee; Wang, Siyang; Tang, Zeping
• Is Part Of: AAOU journal. Volume 11:Issue 2(2016)
• Page Start: 197
• Page End: 215
• Publication Date: 2016-09-05
• Subjects: Clustering -- E-learning -- Density based -- Minimum spanning tree -- Online learning resources; Open learning -- Asia -- Periodicals; Distance education -- Asia -- Periodicals; 371.35095
• DOI: 10.1108/AAOUJ-09-2016-0036
• Languages: English
• ISSNs: 2414-6994

Abstract

Abstract : Purpose: The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues. Design/methodology/approach: In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree. Findings: Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources. Originality/value: The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.