LAOps: Learning Analytics with Privacy-aware MLOps

Pia Niemelä, Bilhanan Silverajan, Mikko Nurminen, Jenni Hukkanen, Hannu-Matti Järvinen

2022

Abstract

The intake of computer science faculty has rapidly increased with simultaneous reductions to course personnel. Presently, the economy is recovering slightly, and students are entering the working life already during their studies. These reasons have fortified demands for flexibility to keep the target graduation time the same as before, even shorten it. Required flexibility is created by increasing distance learning and MOOCs, which challenges students’ self-regulation skills. Teaching methods and systems need to evolve to support students’ progress. At the curriculum design level, such learning analytics tools have already been taken into use. This position paper outlines a next-generation, course-scope analytics tool that utilises data from both the learning management system and Gitlab, which works here as a channel of student submissions. Gitlab provides GitOps, and GitOps will be enhanced with machine learning, thereby transforming as MLOps. MLOps that performs learning analytics, is called here LAOps. For analysis, data is copied to the cloud, and for that, it must be properly protected, after which models are trained and analyses performed. The results are provided to both teachers and students and utilised for personalisation and differentiation of exercises based on students’ skill level.

Paper Citation

in Harvard Style

Niemelä P., Silverajan B., Nurminen M., Hukkanen J. and Järvinen H. (2022). LAOps: Learning Analytics with Privacy-aware MLOps. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-562-3, pages 213-220. DOI: 10.5220/0011113300003182

in Bibtex Style

@conference{csedu22,
author={Pia Niemelä and Bilhanan Silverajan and Mikko Nurminen and Jenni Hukkanen and Hannu-Matti Järvinen},
title={LAOps: Learning Analytics with Privacy-aware MLOps},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2022},
pages={213-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011113300003182},
isbn={978-989-758-562-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - LAOps: Learning Analytics with Privacy-aware MLOps
SN - 978-989-758-562-3
AU - Niemelä P.
AU - Silverajan B.
AU - Nurminen M.
AU - Hukkanen J.
AU - Järvinen H.
PY - 2022
SP - 213
EP - 220
DO - 10.5220/0011113300003182