@inproceedings{40c30aad20894c9daf187ddc6d93a8f7,
title = "LORD: A Moodle Plug-in Helps to Find the Relations Among Learning Objects",
abstract = "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{\textquoteright}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{\textquoteright} 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{\textquoteright} sequential behaviours among learning objects.",
keywords = "Behaviour analysis, Learning path, Munkre{\textquoteright}s Assignment Algorithm, Semantic similarity, Visualization, WordNet",
author = "Rita Kuo and Radomir Wasowski and Ted Krahn and Maiga Chang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 18th International Conference on Intelligent Tutoring Systems, ITS 2022 ; Conference date: 29-06-2022 Through 01-07-2022",
year = "2022",
doi = "10.1007/978-3-031-09680-8\_11",
language = "English",
isbn = "9783031096792",
series = "Lecture Notes in Computer Science",
pages = "115--122",
editor = "Scott Crossley and Elvira Popescu",
booktitle = "Intelligent Tutoring Systems - 18th International Conference, ITS 2022, Proceedings",
}