Analyzing and promoting the regulation of group learning processes among university students
- Start date: 01.01.2017
- Funded by: Universität Augsburg
- Principal investigator: Prof. Dr. Ingo Kollar, Prof. Dr. Markus Dresel
- Participating researchers: Dr. Martin Greisel, Laura Spang
The focus of this project is to examine the question of how individual and collaborative learning processes are regulated in study groups and to what extent these regulatory processes are influenced by individual learning prerequisites. The regulatory activity of students is examined in different learning scenarios, both with regard to different types of strategies and at different levels of regulation (self-, co-, shared-level). Furthermore, the ways in which the adaptive strategy application can be supported instructionally for study groups homogenous vs. heterogenous in knowledge and motivation are investigated.
Student learning often takes place collaboratively (for example, in the form of exam preparation study groups). However, there is little systematic research so far on which regulatory strategies students apply to different problems during collaborative learning and how learning groups respond adaptively to the nature of the problem (Pintrich, 2004). This is of relevance, as it has often been shown that students often do not tap into the full potential of collaborative learning (Weinberger, Stegmann, & Fischer, 2010). In order to ensure the long-term success of student study groups, regulatory processes must therefore be analyzed and promoted as needed.
Two approaches can be used to describe the regulatory processes: First, the research on self-regulated learning, in which typologies of strategies were developed (e.g., Schwinger, Steinmayr, & Spinath, 2012), and second, the research on the co-regulation of collaborative learning that differentiates self-, co-, and shared-level of regulation (Hadwin & Järvelä, 2011). Since research can benefit from the combination of both approaches, the strategy application should be differentiated according to strategy types as well as differentiated by levels. This helps to determine whether students react adaptively, i.e., respond to different problems with appropriate regulatory strategies. In addition, the effects of a training of the adaptive response to various problem situations will be examined.
As part of the research project, we will therefore address the following points:
Which individual learning prerequisites, e.g., different goal orientations of the students (Spinath, Stiensmeier-Pelster, Schöne & Dickhäuser, 2012), influence the engagement in regulatory activities at self-, co- and shared-level, how students adaptively respond to various regulatory challenges, to what extent finding patterns from a first study can be replicated with the inclusion of a measure for strategy recording more close to actual behavior, whether the nature of the group composition (homogeneous vs. heterogeneous study groups) has an influence on the regulatory activity and to what extent the adaptive regulation can be instructionally supported in homo- or heterogeneous groups.
Own resources of the Department of Educational Psychology and the Department of Psychology at the University of Augsburg.
De Backer, L., Kollar, I., Williams, C. A., Seufert, T., Weinberger, A., Melzner, N., Greisel, M., Dresel, M., Kielstra, J., Molenaar, I., Van Keer, H., Valcke, M., & Hämäläinen, R. (2018). Assessing prerequisites and processes of self-, co- and shared regulation during collaborative learning. In J. Kay & R. Luckin (Hrsg.), Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018 (Bd. 2, S. 1296–1303). International Society of the Learning Sciences. See PDF
Greisel, M., Melzner, N., Kollar, I., & Dresel, M. (2018). How groups regulate their learning: The influence of achievement goals on self-, co- and shared regulation strategies. In J. Kay & R. Luckin (Hrsg.), Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018 (Bd. 3, S. 1561–1562). International Society of the Learning Sciences. See PDF
Melzner, N., Greisel, M., Dresel, M., & Kollar, I. (2020). Regulating self-organized collaborative learning: The importance of homogeneous problem perception, immediacy and intensity of strategy use. International Journal of Computer-Supported Collaborative Learning, 15, 149-177. See PDF
Melzner, N., Greisel, M., Dresel, M., & Kollar, I. (2019a). Effective regulation in collaborative learning: An attempt to determine the fit of regulation challenges and strategies. In K. Lund, G. Niccolai, E. Lavoué, C. Hmelo-Silver, G. Gweon, & M. Baker (Hrsg.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) (Bd. 1, S. 312–319). International Society of the Learning Sciences. See PDF
Melzner, N., Greisel, M., Dresel, M., & Kollar, I. (2019b). Using process mining (PM) and epistemic network analysis (ENA) for comparing processes of collaborative problem regulation. In B. Eagan, M. Misfeldt, & A. Siebert-Evenstone (Hrsg.), Advances in Quantitative Ethnography. ICQE 2019 (Bd. 1112, S. 154–164). Springer. See PDF