Towards a data archiving solution for learning analytics

Sarah Taylor; Pablo Munguia

doi:10.1145/3170358.3170415

Towards a data archiving solution for learning analytics

Sarah Taylor, Pablo Munguia

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

6 Citations (Scopus)

Abstract

Data solutions in the teaching and learning space are in need of pro-active innovations in data management, to ensure that systems for learning analytics can scale up to match the size of datasets now available. Here, we illustrate the scale at which a Learning Management System (LMS) accumulates data, and discuss the barriers to using this data for in-depth analyses. We illustrate the exponential growth of our LMS data to represent a single example dataset, and highlight the broader need for taking a pro-active approach to dimensional modelling in learning analytics, anticipating that common learning analytics questions will be computationally expensive, and that the most useful data structures for learning analytics will not necessarily follow those of the source dataset.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Learning Analytics and Knowledge
Subtitle of host publication	Towards User-Centred Learning Analytics, LAK 2018
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	260-264
Number of pages	5
ISBN (Electronic)	9781450364003
DOIs	https://doi.org/10.1145/3170358.3170415
Publication status	Published - 7 Mar 2018
Externally published	Yes
Event	8th International Conference on Learning Analytics and Knowledge, LAK 2018 - Sydney, Australia Duration: 5 Mar 2018 → 9 Mar 2018

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	8th International Conference on Learning Analytics and Knowledge, LAK 2018
Country/Territory	Australia
City	Sydney
Period	5/03/18 → 9/03/18

Keywords

Barriers to adoption
Big data
Data retention
Dimensional modelling
Learning analytics
Learning management systems

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10.1145/3170358.3170415Licence: In Copyright

Cite this

Taylor, S., & Munguia, P. (2018). Towards a data archiving solution for learning analytics. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics, LAK 2018 (pp. 260-264). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3170358.3170415

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Taylor, S & Munguia, P 2018, Towards a data archiving solution for learning analytics. in Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics, LAK 2018. ACM International Conference Proceeding Series, Association for Computing Machinery, New York, NY, pp. 260-264, 8th International Conference on Learning Analytics and Knowledge, LAK 2018, Sydney, Australia, 5/03/18. https://doi.org/10.1145/3170358.3170415

Towards a data archiving solution for learning analytics. / Taylor, Sarah; Munguia, Pablo.
Proceedings of the 8th International Conference on Learning Analytics and Knowledge: Towards User-Centred Learning Analytics, LAK 2018. New York, NY: Association for Computing Machinery, 2018. p. 260-264 (ACM International Conference Proceeding Series).

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

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Towards a data archiving solution for learning analytics

Abstract

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Conference

Keywords

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