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Cohesion network analysis: customized curriculum management in Moodle

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.
1541-1672
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
daebf5ac-ada8-43b7-8f5e-261592e619e4
Choi, Sunhea
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Godfrey, Keith
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Brands, Brigitte Angela
8395273b-4a31-4418-adbe-30cb9be90d57
Koletzko, Berthold
1932e5e8-b045-4e48-aa1e-5e4ea6803a69
Gutu-Robu, Gabriel
0c82967b-d2f5-4af7-b991-7a3fa4dc3108
Mihai, Darius
6b9eb833-80a2-4d81-846f-51d96860a70f
Dascalu, Mihai
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Carabas, Mihai
c564e8e9-e564-4a38-9ac3-34c056ed2180
Trausan-Matu, Stefan
daebf5ac-ada8-43b7-8f5e-261592e619e4
Choi, Sunhea
1d0e766d-38d5-4d01-aea7-639c4334334f
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).

Record type: Article

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.

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ENEA 2020 IEEE v1 - Accepted Manuscript
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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
ORCID for Keith Godfrey: ORCID iD orcid.org/0000-0002-4643-0618

Catalogue record

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: Sunhea Choi
Author: Keith Godfrey ORCID iD
Author: Brigitte Angela Brands
Author: Berthold Koletzko

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