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1st International Workshop on User-Centered Practices of Knowledge Discovery in Educational Data

Published: 28 June 2024 Publication History

Abstract

By offering a large number of highly diverse resources, learning platforms have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promoted a significant growth of machine learning and knowledge discovery approaches and have opened up new opportunities for supporting and assessing educational experiences in a data-driven fashion. Being able to understand students’ behavior and devise models able to provide data-driven decisions pertaining to the learning domain is a primary property of learning platforms, aiming at maximizing learning outcomes. However, the use of knowledge discovery in education also raises a range of ethical challenges including transparency, reliability fairness, and inclusiveness. In this workshop event, we focus on providing a common ground for researchers and practitioners working in this vibrant area, with the ultimate ambitious goal of bridging the UMAP community with the domain-oriented educational sister communities.

References

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Mina Shirvani Boroujeni, Kshitij Sharma, Łukasz Kidziński, Lorenzo Lucignano, and Pierre Dillenbourg. Sept. 13-16, 2016. How to Quantify Student’s Regularity?. In Proc. 11th. Eur. Conf. Technology Enhanced Learning. 277–291. https://doi.org/10.1007/978-3-319-45153-4_21
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Dragan Gasevic, Jelena Jovanovic, Abelardo Pardo, and Shane Dawson. 2017. Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance. J. Learn. Anal. 4, 2 (Jul. 2017), 113–128. https://doi.org/10.18608/jla.2017.42.10
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Severin Klingler, Tanja Käser, Barbara Solenthaler, and Markus Gross. Jun./Jul. 29-2, 2016. Temporally Coherent Clustering of Student Data. In Proc. 9th Int. Conf. Educ. Data Mining. 102–109.
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Paola Mejia-Domenzain, Mirko Marras, Christian Giang, and Tanja Käser. 2022. Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms. In Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I. Springer, 90–102.
[5]
Daniela Rotelli, Anna Monreale, and Riccardo Guidotti. 2022. Uncovering Student Temporal Learning Patterns. In Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption: 17th European Conference on Technology Enhanced Learning, EC-TEL 2022, Toulouse, France, September 12–16, 2022, Proceedings. Springer, 340–353.

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  1. 1st International Workshop on User-Centered Practices of Knowledge Discovery in Educational Data

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          cover image ACM Conferences
          UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
          June 2024
          662 pages
          ISBN:9798400704666
          DOI:10.1145/3631700
          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          Published: 28 June 2024

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