DEKDIV: A Linked-Data-Driven Web Portal for Learning Analytics

Blog

On behalf of our STKO lab, Yingjie won the second prize in the LAK Data Challenge in the International Conference on Learning Analytics and Knowledge (LAK), held in Indianapolis, IN, March 24-28, 2014.

The LAK community focus on the methods and analysis on education-related research topics. The LAK Data Challenge presents data about the publications, researchers, and conferences in the LAK field, and formats the data in a machine-readable manner using Resource Description Framework (RDF). The mission of this challenge is to facilitate the understanding of the LAK discipline, such as the evolution of research topics, as well as the collaborative relations among researchers.

The work presented by our lab is an online scientometrics workbench, called DEKDIV, which employs the state-of-the-art Semantic Web technologies and offers a number of powerful functional modules. Specifically, DEKDIV highlights the following functionalities:

Discovering Geographic Patterns. The Collaborative Institute module (figure 1) represents the coauthorships among research institutes throughout the world and detects some collaboration patterns (e.g., domestic or international collaboration). The Conference Participant module (figure 2) displays the geographic distribution of conference participants throughout the world. The Reference Map module shows the locations of the first authors whose papers have been cited.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Topic Modeling and Researcher Similarity. As the LAK dataset also provides full text for each paper, we employed the Latent Dirichlet Allocation (LDA) to identify the topics contained in the text. We then combine all the papers of each researcher and use LDA to identify their research interests. Consequently, researchers’ interests can be represented as a probability distribution of topics, and we derive their similarity using cosine distance metrics. The similarities of researchers are then displayed using Multidimensional Scaling (MDS), and a screenshot can be seen in figure 3.

Recommending Reviewers and Finding Potential Collaborators. Based on the research interests of scholars in the field of LAK, our workbench also provides the functionality to recommend reviewers to a newly submitted paper (figure 4). The system can automatically extract the key concepts from the abstract of the paper and then find researchers who have similar publications, while avoiding interest conflicts (i.e., excluding researchers who have coauthored together before). The Potential Collaborator module (figure 5) can help find researchers who have similar research interests but who may have never collaborated before.

As a group in the geography department, our work features the functionalities that help discover spatiotemporal patterns from the locations of research institutes and conferences, as well as the geographic relations among them. While we are new to the field of learning analytics, our work received good feedback and comments from the LAK community.