Productive disciplinary engagement characteristics of the highest and lowest performance of a group
1University of Oulu, Faculty of Education, Educational Sciences
|Online Access:||PDF Full Text (PDF, 1.9 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-202006172431
Oulu : P. Santur,
|Publish Date:|| 2020-06-18
|Thesis type:||Master's thesis
The concept of engagement has gained relevance in recent decades due to being positively correlated with student achievement (Pekrun & Linnebrink-Garcia, 2012; Greene, Miller, Crow-son, Duke, & Akey, 2004; Marks, 2000). Researchers have reached a certain consensus in understanding engagement as a meta-construct encompassing at least three dimensions: behavior, emotion, and cognition (Fredricks et al., 2004). However, only few studies have analyzed engagement with a process-oriented approach, measuring the evolution of the different facets during collaborative interaction.
Integrating elements from the Productive Disciplinary Engagement (Engle & Conant, 2002) and Self-Regulated Learning frameworks, Rogat, Cheng, Hmelo-Silver, Adeoye, Gomoll, Traynor, & Lundh, (2019a) developed a rubric including 5 dimensions of engagement: behavioral (Beh), collaborative (Col), socio-emotional (SoE), metacognitive (MeC) and disciplinary (Dis). Using this criterion, the present study explores teacher education students’ productive disciplinary engagement during mathematical tasks. Moreover, the sessions with the highest and lowest learning scores of one group during a mathematics course. Later, these are referred as High-performance session (HPS) and Low-Performance session (LPS), respectively.
Using a process-oriented approach, interaction analysis was used to code the video recordings of the group in both sessions. The coding was done observing the second-by-second variations in each dimension. Every event identified was assigned with one of four levels of quality (low, moderate-low, moderate, or high). Next, a co-occurrence analysis was used to examine the simultaneous variations between dimensions. Finally, the study was later extended to the individual level using inductive analysis, looking for reasons to explain the quality-levels of engagement reached by the group in each session.
The results indicate more consistent higher quality-levels for all facets of engagement in the HPS, and moderate levels in the LPS. In regard to the co-occurrence of quality-level variations of engagement, in both sessions were found four pairs of dimensions that varied synchronously more often (Col-Dis, Beh-Col, Soe-Col, and SoE-Dis) and two less often (MeC-Beh and MeC-SoE). Some features found to influence on the quality-level of engagement were group composition, pre-task knowledge, and the use of the collaborative script.
For researchers, these findings support the claim of engagement as a meta construct composed by different components and provide empirical results for the rubric used by Rogat et al. (2019a). For teachers and educators, this study provides insights to better design of collaborative interactions, providing support for the development of individual and group regulatory skills to increase the quality of engagement.
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