A Lecture Slide Reconstruction System Based on Expertise Extraction for e-Learning

MOOC has brought many benefits to e-learning systems as students are able to obtain various educational presentation slides through digital libraries. These presentation slides provide varying levels of knowledge to specific students. On the other hand, students usually have different levels of knowledge. Thus, it is important to detect expertise levels of lecture slides for specific students, and supplement the lecture slides with related information automatically for different knowledge levels of students. Therefore, we developed a novel automatic slide reconstruction system for digital libraries in e-learning, it generates new lecture contents from one original content related to users’ interests and knowledge levels by adding and removing slides, in order to enable users to learn the reconstructed slides that they do not need no more searching. Our system first extracts topics and groups slides on topics to detect the expertise level of an original content by considering the context in the presentation. The system then searches other necessary contents and determines unnecessary original slide groups based on users’ interests and knowledge levels. Through this, the system can automatically reconstruct lecture slides by classifying them into four groups based on expertise of lecture slides. Those groups are: basic contents for beginners, basic or specialized contents for intermediate students, and specialized contents for advanced students. As a result, users can satisfy and joyfully learn the newly reconstructed slides that are suit to their interests and knowledge levels. In this paper, we discuss our automatic slide reconstruction system to deal with different knowledge levels of students for content understanding, knowledge deepening, and interest-expanding, and verify its effectiveness.