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From Variables to States to Trajectories (VaSSTra): A Method for Modelling the Longitudinal Dynamics of Learning and Behaviour

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Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2022)

Part of the book series: Lecture Notes in Educational Technology ((LNET))

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Abstract

Research in learning analytics needs longitudinal studies that explore the learner’s behaviour, disposition, and learning practices across time, a gap this article aims to bridge. We present VaSSTra: an innovative method for the longitudinal analysis of educational data that can be applied at different time scales (e.g., days, weeks, or courses), and allows the study of different aspects of learning as well as the factors that explain how such aspects evolve over time. Our method combines life-events methods with sequence analysis and consists of three steps: (1) converting variables to states (where variables are grouped into homogenous states); (2) from states to sequences (where the states are used to construct sequences across time), and (3) from sequences to trajectories (where similar sequences are grouped in trajectories). VaSSTra enables us to map the longitudinal unfolding of events while taking advantage of the wealth of life-events methods to visualize, model and describe the temporal dynamics of longitudinal activities. We demonstrate the method with a practical case study example.

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Acknowledgement

This study is partially funded by two Erasmus+ program projects: ENVISION_2027 (grant number 2020–1-FI01-KA226-HE-092653) and ILEDA (2021–1-BG01-KA220-HED-000031121). The paper is co-funded by the Academy of Finland for the project TOPEILA (350560) which was received by the last author.

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Authors and Affiliations

  1. University of Eastern Finland, Joensuu, Finland

    Sonsoles López-Pernas & Mohammed Saqr

  2. Universidad Politécnica de Madrid, Madrid, Spain

    Sonsoles López-Pernas & Mohammed Saqr

Authors
  1. Sonsoles López-Pernas
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  2. Mohammed Saqr
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Correspondence to Sonsoles López-Pernas .

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Editors and Affiliations

  1. GRIAL Research Group, Universidad de Salamanca, Salamanca, Spain

    Francisco José García-Peñalvo

  2. GRIAL Research Group, Universidad de Salamanca, Salamanca, Spain

    Alicia García-Holgado

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López-Pernas, S., Saqr, M. (2023). From Variables to States to Trajectories (VaSSTra): A Method for Modelling the Longitudinal Dynamics of Learning and Behaviour. In: García-Peñalvo, F.J., García-Holgado, A. (eds) Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality. TEEM 2022. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-0942-1_123

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  • DOI: https://doi.org/10.1007/978-981-99-0942-1_123

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0941-4

  • Online ISBN: 978-981-99-0942-1

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