Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/10324
Title: Influences on Current Motivation in Digitally Supported Learning: An Analysis Using Regression and Necessary Condition Analyses
Authors: Moser, Stephanie
Lewalter, Doris
Buchmayer, Iris
Deibl, Ines
Fleischer, Timo
Maier, Simone
Strahl, Alexander
Zumbach, Jörg
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Moser, S., Lewalter, D., Buchmayer, I., Deibl, I., Fleischer, T., Maier, S., Strahl, A., & Zumbach, J. (2023). Influences on current motivation in digitally supported learning: An analysis using regression and necessary condition analyses. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 75-82). International Society of the Learning Sciences.
Abstract: Learners’ current motivation according to the conceptualization of Rheinberg and colleagues is crucial for taking up and pursuing learning activities. Yet, there is still little research regarding the necessary level of factors affecting current motivation itself. This study was designed to start filling this research gap by examining the relative contributions of person-related factors on students’ current motivation to learn with a digitally-supported learning environment, focusing on experiments in chemistry education. The study’s sample consisted of 155 second graders. Multiple linear regression and necessary condition analysis (NCA) were used to investigate possible factors that predict students’ current motivation to learn (i.e., interest and challenge). Results indicate that students’ self-perceived experimentation competence and self-determination positively influence interest, while self-determination alone predicts challenge. However, NCA results reveal all variables as necessary conditions, that set restrictions for the maximum level of interest and challenge. Practical and research related implications are discussed.
Description: Long Paper
URI: https://doi.org/10.22318/icls2023.110098
https://repository.isls.org//handle/1/10324
Appears in Collections:ISLS Annual Meeting 2023

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