Abstract
Learning basic concepts before complex ones is a natural form of learning. Automated systems and instructional designers evaluate and order concepts’ complexity to successfully generate and recommend or adapt learning paths. This paper addresses the specific challenge of accurately and adequately identifying concept prerequisites using semantic web technologies for a basic understanding of a particular concept within the context of learning: given a target concept c, the goals are to (a) find candidate concepts that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our four step approach consists of (i) an exploration of Knowledge Graphs in order to identify possible candidate concepts; (ii) the creation of a set of potential concepts; (iii) deployment of supervised learning model to evaluate a proposed list of prerequisite relationships regarding the target set; and, (iv) validation of our approaching using a ground truth of 80 concepts from different domains (with a precision varying between 76% and 96%).
Highlights
The automatic identification of prerequisite relationships between concepts has been identified as one of the cornerstones for modern, large-scale online educational applications (Gasparetti et al 2018; Talukdar and Cohen 2012; Pan et al 2017)
As all our proposal is based on analyzing the connections between the concepts in the KG (i.e. SemRefD and the features of the supervised learning model), a lower performance was expected in comparison with the other domains considered
There is a constant increase in the number of concepts and relationships that are included in existing knowledge graphs such as DBpedia, Wikidata and YAGO
Summary
The automatic identification of prerequisite relationships between concepts has been identified as one of the cornerstones for modern, large-scale online educational applications (Gasparetti et al 2018; Talukdar and Cohen 2012; Pan et al 2017). Different from previous learning-based methods that require a training set and an extensive feature extraction process, a simple reference distance RefD is proposed by Liang et al (2015) to measure a prerequisite relation among concepts. This measure can be adapted to other contexts and measures of similarity or reference, RefD is less accurate than supervised proposals (Pan et al 2017). As all our proposal is based on analyzing the connections between the concepts in the KG (i.e. SemRefD and the features of the supervised learning model), a lower performance was expected in comparison with the other domains considered. Building broader training sets for the task of identifying prerequisites remains a subject of recent research (Liang et al 2018)
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