Investigating Learning Resources Precedence Relations via Concept Prerequisite Learning

The identification of prerequisite relationships among concepts is a fundamental step toward the organization of knowledge for educational purposes. In the context of a learning process, simplest concepts that are requirements to understand and address more complex concepts should be presented first. Therefore, the identification of prerequisite relationships is a fundamental step for effective course design and automatic learning path generation systems. Although there have been recent advances in machine learning methods for the automatic identification of prerequisite relationships between concepts, little research has been done on whether these automatic strategies can be extended to establish precedence relationships among learning resources. The precedence relation between two learning resources establishes which of the resources must be presented first. In this paper, we approach this problem and propose a strategy to identify the precedence relation. Given two learning resources our strategy analyzes prerequisites among the concepts addressed by the learning resources to estimate the precedence relation. A set of 1588 pairs of learning resources extracted from MOOCs refined by human experts is used to evaluate the strategy. The experimental results show that it is possible to identify the precedence relation between learning resources through the automatic identification of prerequisite relationships between concepts.

[1]  William W. Cohen,et al.  Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia , 2012, BEA@NAACL-HLT.

[2]  Sudeshna Sarkar,et al.  A Comparative Study of Learning Object Metadata, Learning , 2010 .

[3]  Rubén Manrique,et al.  Exploring the Use of Linked Open Data for User Research Interest Modeling , 2017 .

[4]  Diana Maynard,et al.  Interlinking Documents Based on Semantic Graphs with an Application , 2015 .

[5]  Doug Downey,et al.  Local and Global Algorithms for Disambiguation to Wikipedia , 2011, ACL.

[6]  Amit P. Sheth,et al.  Hierarchical interest graph from tweets , 2014, WWW.

[7]  Charles M. Reigeluth,et al.  The elaboration theory’s procedure for designing instruction , 1982 .

[8]  Victor Zue,et al.  Structuring lectures in massive open online courses (MOOCs) for efficient learning by linking similar sections and predicting prerequisites , 2015, INTERSPEECH.

[9]  Ian H. Witten,et al.  An effective, low-cost measure of semantic relatedness obtained from Wikipedia links , 2008 .

[10]  Zhaohui Wu,et al.  Recovering Concept Prerequisite Relations from University Course Dependencies , 2017, AAAI.

[11]  Achim Rettinger,et al.  PageRank on Wikipedia: Towards General Importance Scores for Entities , 2016, @ESWC.

[12]  Bernardo Pereira Nunes,et al.  Towards Automatic Building of Learning Pathways , 2014, WEBIST.

[13]  Chengjiang Li,et al.  Prerequisite Relation Learning for Concepts in MOOCs , 2017, ACL.

[14]  Pablo N. Mendes,et al.  Improving efficiency and accuracy in multilingual entity extraction , 2013, I-SEMANTICS '13.

[15]  C. Lee Giles,et al.  Investigating Active Learning for Concept Prerequisite Learning , 2018, AAAI.

[16]  Wenyi Huang,et al.  Measuring Prerequisite Relations Among Concepts , 2015, EMNLP.

[17]  Carlo De Medio,et al.  Prerequisites between learning objects: Automatic extraction based on a machine learning approach , 2017, Telematics Informatics.

[18]  Zhaohui Wu,et al.  Using Prerequisites to Extract Concept Maps fromTextbooks , 2016, CIKM.

[19]  Rubén Manrique,et al.  How does the size of a document affect linked open data user modeling strategies? , 2017, WI.