Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems

Patrick Seeling

doi:10.1145/3537674.3555780

Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems

Patrick Seeling

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Learning analytics has attracted significant interest for research as well as implementation in education systems. Similar to other analytical approaches to improve underlying processes, the availability of raw data (open-data) of the learning process is paramount. Despite several open-sourced Learning Management System (LMS) solutions that enable an open access to data for learning analytics by/for stakeholders, commercially provided solutions prevalent in the education system in the U.S. are typically closed-source and closed-data. In this contribution, we provide an approach to enable open-sourced, open-data research and implementations within the confined environments of commercial closed-source, closed-data LMS implementations.

Original language	English
Title of host publication	SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education
Publisher	Association for Computing Machinery, Inc
Pages	124-126
Number of pages	3
ISBN (Electronic)	9781450393911
DOIs	https://doi.org/10.1145/3537674.3555780
State	Published - Sep 21 2022
Event	23rd ACM Conference on Information Technology Education, SIGITE 2022 - Chicago, United States Duration: Sep 21 2022 → Sep 24 2022

Publication series

Name	SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education

Conference

Conference	23rd ACM Conference on Information Technology Education, SIGITE 2022
Country/Territory	United States
City	Chicago
Period	09/21/22 → 09/24/22

Keywords

SCORM
e-learning
learning management systems
open-data
open-source

Access to Document

10.1145/3537674.3555780

Cite this

Seeling, P. (2022). Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems. In SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education (pp. 124-126). (SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education). Association for Computing Machinery, Inc. https://doi.org/10.1145/3537674.3555780

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title = "Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems",

abstract = "Learning analytics has attracted significant interest for research as well as implementation in education systems. Similar to other analytical approaches to improve underlying processes, the availability of raw data (open-data) of the learning process is paramount. Despite several open-sourced Learning Management System (LMS) solutions that enable an open access to data for learning analytics by/for stakeholders, commercially provided solutions prevalent in the education system in the U.S. are typically closed-source and closed-data. In this contribution, we provide an approach to enable open-sourced, open-data research and implementations within the confined environments of commercial closed-source, closed-data LMS implementations.",

keywords = "SCORM, e-learning, learning management systems, open-data, open-source",

author = "Patrick Seeling",

note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 23rd ACM Conference on Information Technology Education, SIGITE 2022 ; Conference date: 21-09-2022 Through 24-09-2022",

year = "2022",

month = sep,

day = "21",

doi = "10.1145/3537674.3555780",

language = "English",

series = "SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education",

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Seeling, P 2022, Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems. in SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education. SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education, Association for Computing Machinery, Inc, pp. 124-126, 23rd ACM Conference on Information Technology Education, SIGITE 2022, Chicago, United States, 09/21/22. https://doi.org/10.1145/3537674.3555780

Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems. / Seeling, Patrick.
SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education. Association for Computing Machinery, Inc, 2022. p. 124-126 (SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Learning analytics has attracted significant interest for research as well as implementation in education systems. Similar to other analytical approaches to improve underlying processes, the availability of raw data (open-data) of the learning process is paramount. Despite several open-sourced Learning Management System (LMS) solutions that enable an open access to data for learning analytics by/for stakeholders, commercially provided solutions prevalent in the education system in the U.S. are typically closed-source and closed-data. In this contribution, we provide an approach to enable open-sourced, open-data research and implementations within the confined environments of commercial closed-source, closed-data LMS implementations.

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KW - open-data

KW - open-source

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BT - SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education

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Seeling P. Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems. In SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education. Association for Computing Machinery, Inc. 2022. p. 124-126. (SIGITE 2022 - Proceedings of the 23rd Annual Conference on Information Technology Education). doi: 10.1145/3537674.3555780

Enabling Open Source, Open Data for Closed Source, Closed Data Learning Management Systems

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