Search

PurposeThe 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/approachIn 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.FindingsConventional 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/valueThe 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.

The information embedded in core academic journals is useful to learn effectively, recommend prestigious learning resources, and reflect the academic level of subjects in some areas. This article involves 11 Guangdong medical academic journals published from 1989 to 2015, which are collected from the Chinese Science Citation Database. We divide these journals into 4 small subjects and analyze them from both the time and subject dimensions. From the time dimension, the total cited curve presents an exponential trend. The annual averaged cited quantities are larger than those in the past. The earlier papers are published, the longer the interval between the published year and the year that reaches the cited peak. From the subject dimension, the cited curve of traditional Chinese medicine is apparently different from the other 3 subjects. These journals overall appear relatively consistent cited characteristics. The citation theory of periodical data can help us understand the citation rule of online learning data and use them more efficiently.

With the rapid progress and development of society and scientific research and development, the hysteresis quality of Digital library document retrieval service platform has affected the innovation ability of scientific research at home and abroad to some extent. In order to improve the efficiency of scientific communication, information sharing and the efficiency of starting the emergency plan, we will break the traditional digital library and library service mode to build a new monitoring service platform that is based on the inference of public opinion with science and technology. According to monitoring and collection of information data from the user-operated media such as micro-blog and WeChat, we do some analysis and inference of the data so as to make this platform widely used in science and Technology Information Research team and related enterprises. The platform is useful to acquire the latest scientific research and can greatly enhance the ability of scientific research and innovation at home and abroad.