Cohesion network analysis: customized curriculum management in Moodle
Cohesion network analysis: customized curriculum management in Moodle
Learning Management Systems frequently act as platforms for online content which is usually structured hierarchically into modules and lessons to ease navigation. However, the volume of information may be overwhelming, or only part of the lessons may be relevant for an individual; thus, the need for customized curricula emerges. We introduce a Moodle plugin developed to help learners customize their curriculum to best fit their learning needs by relying on specific filtering criteria and semantic relatedness. For this experiment, a Moodle instance was created for doctors working in the field of nutrition in early life. The platform includes 78 lessons tackling a wide variety of topics, organized into five modules. Our plugin enables users to specify basic filtering criteria, including their field of expertise, topics of interest from a predefined taxonomy, or expected themes (e.g., background knowledge, practice & counselling, or guidelines) for a preliminary pre-screening of lessons. In addition, learners can also provide a description in natural language of their learning interests. This text is compared with each lesson’s description using Cohesion Network Analysis, and lessons are selected above an experimentally set threshold. Our approach also takes into account prior knowledge requirements, and may suggest lessons for further reading. Overall, the plugin covers the management of the entire course lifecycle, namely: a) creating a customized curriculum; b) tracking the progress of completed lessons; c) generating completion certificates with corresponding CME points.
Gutu-Robu, Gabriel
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Mihai, Darius
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Dascalu, Mihai
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Carabas, Mihai
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Trausan-Matu, Stefan
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Choi, Sunhea
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Godfrey, Keith
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Brands, Brigitte Angela
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Koletzko, Berthold
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24 February 2021
Gutu-Robu, Gabriel
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Mihai, Darius
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Dascalu, Mihai
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Carabas, Mihai
c564e8e9-e564-4a38-9ac3-34c056ed2180
Trausan-Matu, Stefan
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Choi, Sunhea
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Godfrey, Keith
0931701e-fe2c-44b5-8f0d-ec5c7477a6fd
Brands, Brigitte Angela
8395273b-4a31-4418-adbe-30cb9be90d57
Koletzko, Berthold
1932e5e8-b045-4e48-aa1e-5e4ea6803a69
Gutu-Robu, Gabriel, Mihai, Darius, Dascalu, Mihai, Carabas, Mihai, Trausan-Matu, Stefan, Choi, Sunhea, Godfrey, Keith, Brands, Brigitte Angela and Koletzko, Berthold
(2021)
Cohesion network analysis: customized curriculum management in Moodle.
IEEE Intelligent Systems.
(doi:10.1109/SYNASC51798.2020.00037).
Abstract
Learning Management Systems frequently act as platforms for online content which is usually structured hierarchically into modules and lessons to ease navigation. However, the volume of information may be overwhelming, or only part of the lessons may be relevant for an individual; thus, the need for customized curricula emerges. We introduce a Moodle plugin developed to help learners customize their curriculum to best fit their learning needs by relying on specific filtering criteria and semantic relatedness. For this experiment, a Moodle instance was created for doctors working in the field of nutrition in early life. The platform includes 78 lessons tackling a wide variety of topics, organized into five modules. Our plugin enables users to specify basic filtering criteria, including their field of expertise, topics of interest from a predefined taxonomy, or expected themes (e.g., background knowledge, practice & counselling, or guidelines) for a preliminary pre-screening of lessons. In addition, learners can also provide a description in natural language of their learning interests. This text is compared with each lesson’s description using Cohesion Network Analysis, and lessons are selected above an experimentally set threshold. Our approach also takes into account prior knowledge requirements, and may suggest lessons for further reading. Overall, the plugin covers the management of the entire course lifecycle, namely: a) creating a customized curriculum; b) tracking the progress of completed lessons; c) generating completion certificates with corresponding CME points.
Text
ENEA 2020 IEEE v1
- Accepted Manuscript
More information
Accepted/In Press date: 11 August 2020
e-pub ahead of print date: 1 September 2020
Published date: 24 February 2021
Identifiers
Local EPrints ID: 443307
URI: http://eprints.soton.ac.uk/id/eprint/443307
ISSN: 1541-1672
PURE UUID: f0cff56e-55ae-474d-9267-e8e3b3beb2d5
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Date deposited: 20 Aug 2020 16:31
Last modified: 17 Mar 2024 05:50
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Contributors
Author:
Gabriel Gutu-Robu
Author:
Darius Mihai
Author:
Mihai Dascalu
Author:
Mihai Carabas
Author:
Stefan Trausan-Matu
Author:
Brigitte Angela Brands
Author:
Berthold Koletzko
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