Several studies have highlighted the positive effects that active learning may have on student engagement and performance. However, the influence of active learning strategies is mediated by several factors, including the nature of the learning environment and the cognitive level of in-class tasks. These factors can affect different dimensions of student engagement such as the nature of social processing in student groups, how knowledge is used and elaborated upon by students during in-class tasks, and the amount of student participation in group activities. In this study involving four universities in the US, we explored the association between these different dimensions of student engagement and the cognitive level of assigned tasks in five distinct general chemistry learning environments where students were engaged in group activities in diverse ways. Our analysis revealed a significant association between task level and student engagement. Retrieval tasks often led to a significantly higher number of instances of no interaction between students and individualistic work, and a lower number of knowledge construction and collaborative episodes with full student participation. Analysis tasks, on the other hand, were significantly linked to more instances of knowledge construction and collaboration with full group participation. Tasks at the comprehension level were distinctive in their association with more instances of knowledge application and multiple types of social processing. The results of our study suggest that other factors such as the nature of the curriculum, task timing, and class setting may also affect student engagement during group work.
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This content will become publicly available on October 22, 2024
Characterising Individual-Level Collaborative Learning Behaviours Using Ordered Network Analysis and Wearable Sensors
Wearable positioning sensors are enabling unprecedented opportunities to model students’ procedural and social behaviours during collaborative learning tasks in physical learning spaces. Emerging work in this area has mainly focused on modelling group-level interactions from low-level x-y positioning data. Yet, little work has utilised such data to automatically identify individual-level differences among students working in co-located groups in terms of procedural and social aspects such as task prioritisation and collaboration dynamics, respectively. To address this gap, this study characterised key differences among 124 students’ procedural and social behaviours according to their perceived stress, collaboration, and task satisfaction during a complex group task using wearable positioning sensors and ordered networked analysis. The results revealed that students who demonstrated more collaborative behaviours were associated with lower stress and higher collaboration satisfaction. Interestingly, students who worked individually on the primary and secondary learning tasks reported lower and higher task satisfaction, respectively. These findings can deepen our understanding of students’ individual-level behaviours and experiences while learning in groups.
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- Award ID(s):
- 2201723
- NSF-PAR ID:
- 10539639
- Publisher / Repository:
- Springer, Cham
- Date Published:
- Edition / Version:
- 1
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 1865-0929
- ISBN:
- 978-3-031-47014-1
- Page Range / eLocation ID:
- 66-80
- Subject(s) / Keyword(s):
- Collaborative Learning Learning Analytics Educational Data Mining Ordered Network Analysis Stress Satisfaction
- Format(s):
- Medium: X Size: 1011KB Other: pdf
- Size(s):
- 1011KB
- Location:
- Melbourne, VIC, Australia
- Sponsoring Org:
- National Science Foundation
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