scholarly journals Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning

2019 ◽  
Vol 14 (4) ◽  
pp. 425-441 ◽  
Author(s):  
Sanna Järvelä ◽  
Hanna Järvenoja ◽  
Jonna Malmberg
Keyword(s):  
Collaborative Learning ◽  
Self Regulation ◽  
Learning Context ◽  
Computer Supported Collaborative Learning ◽  
In Situ Data ◽  
Complex Process ◽  
The Social ◽  
Made In ◽  
Socially Shared Regulation

AbstractSelf-regulation is critical for successful learning, and socially shared regulation contributes to productive collaborative learning. The problem is that the psychological processes at the foundation of regulation are invisible and, thus, very challenging to understand, support, and influence. The aim of this paper is to review the progress in socially shared regulation research data collection methods for trying to understand the complex process of regulation in the social learning context, for example, collaborative learning and computer-supported collaborative learning. We highlight the importance of tracing the sequential and temporal characteristics of regulation in learning by focusing on data for individual- and group-level shared regulatory activities that use technological research tools and by gathering in-situ data about students’ challenges that provoke regulation of learning. We explain how we understand regulation in a social context, argue why methodological progress is needed, and review the progress made in researching regulation of learning.

Fostering Collaboration in CSCL

2009 ◽  
pp. 335-340 ◽  
Author(s):  
Donatella Persico ◽  
Francesca Pozzi ◽  
Luigi Sarti
Keyword(s):  
Online Learning ◽  
Collaborative Learning ◽  
Learning Communities ◽  
Learning Community ◽  
Learning Strategies ◽  
Online Courses ◽  
Computer Supported Collaborative Learning ◽  
Online Learning Communities ◽  
Face To Face ◽  
The Social

Some collaborative learning strategies widely used in face-to-face settings can also be adapted to online contexts. They allow us to master the complex relations between members of large, heterogeneous online learning communities. The authors build on their experience in the application of some of the most well-known strategies and techniques used in online courses, such as jigsaw, peer review, role-play, case study, and brainstorming. The use of these strategies in computer supported collaborative learning (CSCL) environments and the related models describing the social structure of the learning community is discussed in the attempt to highlight their strengths and weaknesses and investigate the conditions for their applicability. The aim is to inform the design and the management of online learning communities.

scholarly journals Identifying areas prone to coastal hypoxia – the role of topography

2019 ◽  
Author(s):  
Elina A. Virtanen ◽  
Alf Norkko ◽  
Antonia Nyström Sandman ◽  
Markku Viitasalo
Keyword(s):  
Oxygen Deficiency ◽  
Coastal Areas ◽  
Management Actions ◽  
In Situ Data ◽  
Efficient Management ◽  
Stockholm Archipelago ◽  
Oxygen Dynamics ◽  
Made In

Abstract. Hypoxia is an increasing problem in marine ecosystems around the world, and recent projections indicate that anoxic dead zones will be spreading in the forthcoming decades. While major advances have been made in our understanding of the drivers of hypoxia, it fundamentally hinges on patterns of water circulation that can be difficult to resolve in coastal regions. The complexity of many coastal areas and lack of detailed in situ data has hindered the development of models describing oxygen dynamics at a sufficient resolution for efficient management actions to take place. We hypothesized that the enclosed nature of seafloors facilitates hypoxia formation. We developed simple proxies of seafloor heterogeneity and modelled oxygen deficiency in complex coastal areas in the northern Baltic Sea. We discovered that topographically sheltered seafloors and sinkholes with stagnant water are prone to the development of hypoxia. Approximately half of the monitoring sites in Stockholm Archipelago and one third of sites in southern Finland experienced severe hypoxia (O2 

Computer-Mediated Communication Learning Environments

2010 ◽  
pp. 38-44
Author(s):  
Stefania Manca
Keyword(s):  
Collaborative Learning ◽  
Learning Strategy ◽  
Computer Mediated Communication ◽  
Social Dimension ◽  
Computer Supported Collaborative Learning ◽  
Mediated Communication ◽  
Model Learning ◽  
Web Based ◽  
Computer Mediated ◽  
The Social

The social, relational, and affective dynamics are receiving more and more attention in the study of learning processes, as cognitive, affective, and emotional dimensions of learning seem to be closely related. This kind of co-origination, borne out in the context of neurosciences, artificial intelligence, cognitive psychology, and education, has also been recognized in the field of Web-based learning. The research framework of computer supported collaborative learning (CSCL) has emphasized the role that a well-established social dimension plays in collaborative learning and group-based working within communities of learners. According to the socioconstructivist model, learning always implies a social dialogical process where individuals are mutually engaged in the construction and sharing of new knowledge (Scardamalia & Bereiter, 1994; Wenger, 1998). Pedagogical approaches based on these assumptions combine the advantages of a learning strategy that promotes deeper level learning, critical thinking, and shared understanding with those related to the development of social and communication skills (Garrison & Anderson, 2003).

Computer-Supported Collaborative Learning

2006 ◽  
pp. 105-111
Author(s):  
Vladan Devedi
Keyword(s):  
Collaborative Learning ◽  
Learning Community ◽  
Information And Communication Technologies ◽  
Student Evaluation ◽  
Online Discussions ◽  
Computer Supported Collaborative Learning ◽  
Learning Groups ◽  
Shared Workspaces ◽  
The Social ◽  
Information And Communication

In computer-supported collaborative learning (CSCL), information and communication technologies are used to promote connections between one learner and other learners, between learners and tutors, and between a learning community and its learning resources. CSCL is a coordinated, synchronous activity of a group of learners resulting from their continued attempt to construct and maintain a shared conception of a problem (Roschelle & Teasley, 1995). CSCL systems offer software replicas of many of the classic classroom resources and activities (Soller, 2001). For example, such systems may provide electronic shared workspaces, on-line presentations, lecture notes, reference material, quizzes, student evaluation scores, and facilities for chat or online discussions. This closely reflects a typical collaborative learning situation in the classroom, where the learners participating to learning groups encourage each other to ask questions, explain and justify their opinions, articulate their reasoning, and elaborate and reflect upon their knowledge, thereby motivating and improving learning. These observations stipulate both the social context and the social processes as an integral part of collaborative learning activities. In other words, CSCL is a natural process of social interaction and communication among the learners in a group while they are learning by solving common problems.

Exploring Socially Shared Regulation in the Context of Collaboration

2013 ◽  
Vol 12 (3) ◽  
pp. 267-286 ◽  
Author(s):  
Sanna Järvelä ◽  
Hanna Järvenoja ◽  
Jonna Malmberg ◽  
Allyson F. Hadwin
Keyword(s):  
Collaborative Learning ◽  
Self Regulation ◽  
Empirical Work ◽  
Group Processes ◽  
Learning Performance ◽  
Collaborative Group ◽  
Social Aspect ◽  
Team Members ◽  
Multiple Challenges ◽  
Socially Shared Regulation

Socially shared regulation of learning refers to processes by which group members regulate their collective activity. Successful individuals regulate their motivational, cognitive, and metacognitive engagement. Our hypothesis is that successful groups also share in regulating group processes. Following our earlier conceptual and empirical work on the social aspect of motivating and regulating learning (Hadwin & Järvelä, 2011; Järvenoja & Järvelä, 2009; Järvelä, Volet, & Järvenoja, 2010), our research questions are as follows: (a) What challenges do individuals and groups report experiencing during collaborative group work? (b) How do students collectively regulate these challenges at the time, and in future collaborations? (c) How do collaborative learning outcomes compare between groups with varying degrees of emerging shared regulation? We present an empirical study in which 18 graduate students worked in collaborative teams of 3–4 over an 8-week period. The nStudy (Winne, Hadwin, & Beaudoin, 2010) software was used for collaborative planning and work, as well as face-to-face and online collaboration between team members. Data included individual and collaborative statements about collaborative challenges, collaborative statements about contextual and future regulation strategies, collaborative learning performance, and log file traces of students’ contributions to collaborative chat discussions and planning activities. Findings indicated that the students expressed multiple challenges resulting in 3 kinds of regulation over time profiles: strong, progressive, and weak shared regulation. We also conclude that successful collaboration not only requires self-regulation but also allows each team member to support fellow team members to successfully regulate their learning and the team to come together to collectively regulate learning.

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