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LAOps: Learning Analytics with Privacy-aware MLOps

Topics: Architectures for AI-based Educational Systems; Cloud-Based Learning and Assessment; Educational Data Mining; Feedback and Learning Support; Intelligent Tutoring Systems; Learning Analytics and Educational Data Mining; Learning with AI Systems; Machine Learning; Next Generation Teaching and Learning Environments; Tools to Assess Learning

Authors: Pia Niemelä ; Bilhanan Silverajan ; Mikko Nurminen ; Jenni Hukkanen and Hannu-Matti Järvinen

Affiliation: Faculty of Information Technology and Communication Sciences, Tampere University, P.O. Box 1001 FI-33014, Tampere, Finland

Keyword(s): Learning Management System, Next-generation Learning Environment, Assessment and Feedback, Learning Analytics, Personalisation, Machine Learning, Privacy-aware Machine Learning, Cloud-based Learning Analysis, MLOps, LAOps.

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 analytic s, 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. (More)

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Paper citation in several formats:
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; ISSN 2184-5026, SciTePress, pages 213-220. DOI: 10.5220/0011113300003182

@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},
issn={2184-5026},
}

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
IS - 2184-5026
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
PB - SciTePress