Online learning resources finder based on computer programming domain

Abstract

With the huge growth of the internet, the amount of content on the internet also grown. Within that context, there are many irrelevant contents spread within the internet for a given topic. Therefore, it is very hard to find accurate, informative learning resources. Even though there are some search engines available, the job they do is very generic and provide millions of search results. Finding the most important learning content within a large set of search results is an extremely difficult task. The solution proposed in this paper addresses this issue. The learner can search for what is required and the system would filter both text and video content across the internet to provide the most relevant content. This paper describes how a textual resource finder was implemented based on ontologies, Euclidean distance, and the TF-IDF algorithm. The video content analyzer used a deep learning algorithm. The solution was developed for learners in the Computer Programming domain.