LORD: A Moodle Plug-in Helps to Find the Relations Among Learning Objects

Rita Kuo, Radomir Wasowski, Ted Krahn, Maiga Chang

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

2 Citations (Scopus)


Learning Management System (LMS) is widely used in higher education. Researchers have proposed methods to analyze the relations among learning objects (i.e., re-sources/activities) of a course in the LMS and then construct the graph structure for the learning objects. Student’s learning behaviour in the LMS can be represented and analysed in graph, i.e., the Learning Object Graph (LOG). With the LOGs represent different students’ learning behaviours, plug-in is designed to cluster students into groups based on their learning behaviours. Such method requires the relations among learning objects can be identified and measured accurate and properly. This research explains how the LORD (Learning Object Relation Discovery) Moodle plug-in measures the similarity between two learning objects, with the help of WordNet and Natural Language Processing, according to their content in English, French and Hindi to create a more reasonable and objective Learning Object Graph (LOG) that can be used to represent students’ sequential behaviours among learning objects.

Original languageEnglish
Title of host publicationIntelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings
EditorsScott Crossley, Elvira Popescu
Number of pages8
Publication statusPublished - 2022
Event18th International Conference on Intelligent Tutoring Systems, ITS 2022 - Virtual, Online
Duration: 29 Jun. 20221 Jul. 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13284 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Intelligent Tutoring Systems, ITS 2022
CityVirtual, Online


  • Behaviour analysis
  • Learning path
  • Munkre’s Assignment Algorithm
  • Semantic similarity
  • Visualization
  • WordNet


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