SHARP

Project description

Socially shared regulation of complex learning process in groups (SHARP)

 

Self-regulation is an invisible demanding mental skill. Essentially it is a skill that individuals must acquire in order to function (socially, emotionally, cognitively) in the world,  but the problem is that the processes at the foundation of regulation are invisible and thus very challenging to understand, support, and influence. While self-regulation of learning (SRL) is difficult for individuals, it is even more so when interacting with peers and in teams; co-regulation (CoRL) and socially shared regulation (SSRL) respectively. SHARP aims to identify and support the regulation of complex learning process in groups in such a way that the individuals also acquire the skills themselves. To do this, we target trigger regulation moments in collaboration by analyzing input from various multimodal sources (i.e., 360°video, log data, heart rate, skin conductivity, self-report) making invisible mental cognitive, motivational, and emotional learning processes visible to both learners and teachers to allow them to regulate those processes and learn more efficiently.

 

SHARP conducts exploratory and intervention research following the collaboration progress of 16-18 year old high school students (experimental/control groups N=360) working in advanced physics. Experimental groups are supported by dashboards designed to prompt SRL, CoRL, and SSRL. SHARP extends the existing methodologies using cutting edge physiological data. It implements temporal data analysis and triangulation of multimodal data to gain insight in challenging trigger regulation moments and changes in learning activities. SHARP uses educational data mining and learning analytics (LA) to transform the data into learning patterns and visualize them via dashboards.

 

As multidisciplinary cutting-edge project, SHARP achieves a significant scientific breakthrough in understanding the group learning process. Through the analysis of its unique multimodal dataset, it complements current understanding of how people learn alone and with others. The methods and tools used are based on LA’s potential to trace and model learning processes and make them visible. In sum, SHARP enriches global efforts to advance theory underlying the cognitive, social, and emotional components of individual and group learning (e.g., SSRL ). It brings completely new technological solutions and digital tools for increasing human competence building and preparing young people to meet the demands of a 21st-century knowledge society.

 

PI Prof. Sanna Järvelä (contact for more information)

Co-PI Ass. Prof. (tenure) Hanna Järvenoja

Co-PI Dr. Jonna Malmberg

 

Project funding: Finnish Academy 2019-2024

 

Collaborators:

Prof. Allyson Hadwin,  Technology Integration and Evaluation Lab, University of Victoria, Canada

Prof. Roger Azevedo, University Central Florida, Learning Sciences Faculty Cluster, US 

Prof. Maria Bannert, Technical University Munich, Germany

Ass. Prof. Inge Molenaar, Radboud University, Nijmegen, The Netherlands

Prof. Dragan Gasevic, Monash University, Australia