The ManyMoments project aims to improve the replicability and generalizability of the findings obtained with intensive longitudinal data (e.g., experience sampling method, ambulatory assessment, etc.).

For that purpose, we

  • compile information about tools that help improving the replicability and generalizability in the work with intensive longitudinal data
  • conduct multi-lab data collections with the experience sampling method in multiple contexts (e.g., multiple universities, multiple countries, etc)
  • plan to conduct many-analysts re-analysis of existing ESM datasets pooled across multiple data sources to test whether published findings replicate and generalize
  • develop analytical methods for a better operationalization of replicability and generalizability in intensive longitudinal data
  • initiate discussions about the need and solutions for more replicable and generalizable ESM research in conferences and meetings.

A first multi-lab data collection using the experience sampling method on the topic of situational learning motivation and learning-relevant emotions in university lectures is currently in preparation.

In its first empirical study, the ManyMoments project will investigate situated academic motivation and learning-relevant emotions in university lectures. In doing so, we will investigate the short-term development of situational anticipatory and value experiences according to Eccles' value-expectancy theory, as well as explore heterogeneity or generalizability across individuals, time points, and contexts.

For this, we are currently looking for researchers who would like to give lectures and observe their students' motivation over a semester.

Feel free to contact us at if you would like to participate.


  • Moeller, J., Langener, A., Lafit, G., Karhulahti, V.-M., Bastiaansen, J., & Bergmann, C. (revise & resubmit). The Hypository: Registering Hypotheses for Cumulative Science. Manuscript submitted for publication. Show article
  • Moeller, J., Dietrich, J., Neubauer, A. B., Brose, A., Kühnel, J., Baars, J., Dehne, M., Jähne, M. F., Schmiedek, F., Bellhäuser, H., Malmberg, L.-E., Stockinger, K., Riediger, M., & Pekrun, R. (2022, April 19). Generalizability Crisis Meets Heterogeneity Revolution: Determining Under Which Boundary Conditions Findings Replicate and Generalize. Show article
  • Moeller, J., Baars, J., & Dietrich, J. (pre print). The Experience Sampling Method in the research on achievement-related emotions and motivation. In: R.C. Lazarides, Hagenauer, H. Järvenoja (Eds.), Motivation and Emotion in Learning and Teaching across Educational Contexts: Theoretical and Methodological Perspectives and Empirical Insights. Routledge. Show article

Conference presentations

  • Julia Moeller & the ManyMomentsConsortium (October 28, 2022). ManyMoments: Generalizability & replicability in intensive longitudinal studies. 2022 Big Team Science Conference (virtually)
  • Julia Moeller, Christina Bergmann, & the ManyMoments Consortium (September 15, 2022). ManyMoments – Improving the replicability and generalizability of intensive longitudinal studies. Symposium at the 52nd Congress of the German Psychological Society (DGPs) in Hildesheim
  • Julia Moeller, Anna Langener, Ginette Lafit, Veli-Matti Karhulahti, Jojanneke A. Bastiaansen, Christina Bergmann (June 28, 2022). Workshop:  Introducing the Hypository: A Tool to Register Hypotheses for Cumulative. ScienceUnconference at the 2022 meeting of the Society for the Improvement of Psychological Science (SIPS).
  • Moeller, J. & Bergmann, C. (June 25, 2021). ManyMoments - Improving the replicability and generalizability of intensive longitudinal studies. Unconference at the 2021 meeting of the Society for the Improvement of Psychological Science (SIPS).
  • The ManyMoments Consortium (2021). ManyMoments - Using Multi-Lab Collaboration to Improve Replicability of Intensive Longitudinal Studies. Paper presented at he 2021 meeting of the Society for Ambulatory Assessment.

How you can participate in our study
If you are teaching lectures or seminars in the upcoming summer semester, we would like to record learning motivation and academic emotions in your courses over the course of the semester.
For you, this would have the following benefits:

  • You will receive immediate feedback on the current learning motivation and academic emotions in your course after each survey (automated via a computer dashboard).
  • You will receive the ESM data set of your own course for your own scientific or didactical use.
  • You become a co-author:in in our ManyMoments article, in which the overall dataset of all participating courses is examined for generalizability and other methodological issues

If you would like to participate in this study or need more information, please feel free to email: manymoments(at)