B3 Non-refereed conference proceedings
How can learning experiences be explored in simulation-based learning situations? (2022)

Karjalainen, S., Silvennoinen, M., Manu, M., Malinen, A., Parviainen, T., & Vesisenaho, M. (2022). How can learning experiences be explored in simulation-based learning situations?. In EAPRIL 2021 Conference Proceedings (pp. 231-243). European Association for Practitioner Research. EAPRIL Conference Proceedings. https://eapril.org/assets/images/proceedings_2021.pdf

JYU authors or editors

Publication details

All authors or editors: Karjalainen, Suvi; Silvennoinen, Minna; Manu, Mari; Malinen, Anita; Parviainen, Tiina; Vesisenaho, Mikko

Parent publication: EAPRIL 2021 Conference Proceedings

Place and date of conference: Online, 24.-26.11.2021

Journal or series: EAPRIL Conference Proceedings

eISSN: 2406-4653

Publication year: 2022

Issue number: 7

Pages range: 231-243

Publisher: European Association for Practitioner Research

Publication country: Belgium

Publication language: English

Persistent website address: https://eapril.org/assets/images/proceedings_2021.pdf

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/86271

Abstract

The aim of our research is to investigate what methods can be used to explore learning experiences. In this case example, we describe how we extracted quantitative and qualitative data reflecting learning experiences from simulation-based learning (SBL) situations. Data collection was conducted in the fields of aviation and forestry. After the SBL situation, the students participated in a stimulated recall interview. The transcribed interview data were analysed using data-driven methods. To capture the dynamics in the (neuro)physiological signals associated with varying states of learning experiences, we recorded activity of the autonomic and central nervous systems. When analysing (neuro)physiological data, we focused on extracting reliable signatures reflecting both the state and the reactivity of the autonomic and central nervous systems. Later on, different data types will be integrated and analysed together. The aim of this article is to elaborate the extent to which different data types can be integrated in analysis to produce meaningful information about learning experiences. Our results based on the students’ interviews highlight the meaningfulness of the instructor’s guidance in SBL situations. We also show that it is possible to extract reliable features from (neuro)physiological signals measured during natural learning situations. These (neuro)physiological features also seem to vary depending on the phase of the simulation. Therefore, we conclude that by including (neuro)physiological measurements in research designs, it is possible to achieve a more comprehensive understanding of learning experiences. This type of multidisciplinary research is likely to provide novel insights in developing learning environments and guidance.

Keywords: learning; learning experiences; learning environment; learning styles; data types; measurement; measuring methods; research; research methods; simulation; neurophysiology

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2022