Automatic Learning Path Creation Using OER: A Systematic Literature Mapping

Abstract

Learning paths are curated sequences of resources organized in a way that a learner has all the prerequisite knowledge needed to achieve their learning goals. In this article, we systematically map the techniques and algorithms that are needed to create such learning paths automatically. We focus on open educational resources (OER), though a similar approach can be used with other types of learning objects. Our method of mapping goes through three passes of selected literature. First, we selected all articles mentioning OER and machine learning from IEEE, SCOPUS, and ACM. This resulted in a set of 347 papers after removing duplicates. Of these, 13 were selected as relating to learning paths and their references and citations were identified and organized into eight categories identified in this article (metadata, linked data, recommendation systems, concept maps, knowledge graphs, classification, and learning paths). After identifying these topics, a manual review was conducted resulting in the final set of 112 papers. This article combines the found categories into three steps for learning path creation, which are then discussed in detail. These steps are as follows: 1) concept extraction; 2) relationship mapping; and 3) path creation. Current research relates primarily to enhancing concept extraction and relationship mapping. We identify directions for potential future research that focus on automatically augmenting previously created learning paths in accordance with the changing needs of learners.