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Enterprise AI Analysis: Implementing learning design with learning analytics to scaffold the regulation of collaboration in primary education

Enterprise AI Analysis

Implementing learning design with learning analytics to scaffold the regulation of collaboration in primary education

The regulation of learning, including self-regulated learning (SRL) and socially shared regulation of learning (SSRL), is a key skill that should already be practiced in primary education. The shift toward student-centered, collaborative, and open-ended processes, such as phenomenon-based learning, increases the need for pupils to take charge of their own learning. However, due to the varying abilities of primary school pupils to regulate their learning, such processes can be challenging and require substantial scaffolding. To address this issue, this study explores how a learning design that indirectly scaffolds the regulation of collaboration can support regulation during the challenging phases of a collaborative blended learning process in primary school. Additionally, the potential of learning analytics (LA) to further support pupils' processes is discussed. A specific study module on sustainable development was designed and implemented in a learning management system (LMS). Classroom observations and LMS log data were collected during the implementation with fifth- and sixth-grade pupils. The observation data show that the most challenging phases of the group work occurred at the beginning of the group process and again during the finalization of the group project. The log data further indicate that pupils used the LMS as a regulation support at the beginning of the process. Overall, the results highlight the need for more functional tools that help groups to metacognitively monitor and summarize their learning. In addition, LA could provide meaningful visualizations for teachers to further develop learning designs during learning processes.

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Executive Impact & Strategic Imperatives

Leverage advanced learning design and analytics to transform educational outcomes, fostering self-regulated and collaborative learning in critical phases. Our AI-driven insights provide the tools needed to optimize pedagogical strategies and ensure continuous improvement.

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Deep Analysis & Enterprise Applications

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Regulation of Learning

Self-regulated learning (SRL) involves individual students systematically activating and sustaining their thoughts, feelings, motivations, and actions to attain objectives. Socially shared regulation of learning (SSRL) describes regulation occurring collectively during group work, where members jointly plan, monitor, and reflect to achieve common outcomes. Both SRL and SSRL are intertwined and crucial for successful collaborative learning, especially in primary education where skills vary. Learners typically do not engage in self-regulated learning spontaneously, highlighting the need for effective learning design and digital tools for scaffolding.

Learning Design

Phenomenon-based learning, common in Finnish education, emphasizes holistic and inquiry-driven processes. Properly planned learning design, incorporating pedagogical elements such as activating prior knowledge, providing complex open-ended problems, and integrating tasks into real-life contexts, supports SRL. Student-centered environments with choices promote motivation and SRL. Learning design operationalizes pedagogical principles and structures digital materials (texts, videos, assignments) to guide pupils' actions towards SRL strategies. It forms a crucial link between learning sciences and environments and provides the theoretical framework for analyzing learning analytics data.

Learning Analytics

Learning analytics (LA) and artificial intelligence (AI) offer data-driven insights and feedback to scaffold learning. LA can provide feedback on how learning design is enacted, revealing pupils' needs across different phases of collaborative learning through temporal and sequential analysis of LMS interaction data (e.g., accessing tasks, monitoring progress, reflections). Insights from LA can help educators evaluate learning designs, make real-time interventions, and identify challenges or misconceptions. Future adaptive LMS systems and AI-based tools like tutoring chatbots can provide dynamic, interactive scaffolding, enhancing SRL and SSRL in student-centered learning processes.

Enterprise Process Flow

Start of the study module
Start of group work
Manuscript
Producing the artifact
Producing continues
Editing and formatting
Show time
Final assignment
2589 LMS Log Entries Analyzed

LMS Use Patterns: Fifth vs. Sixth Graders

Aspect Fifth Graders Sixth Graders
Engagement Pattern
  • Systematic & comprehensive, followed teacher instructions
  • Selective & strategic, accessed materials only when necessary
Scaffolding Reliance
  • More teacher-driven, relied on LMS for guidance
  • More distributed regulation, fewer relied on LMS as collective resource
Challenges Faced
  • More challenges, particularly at the beginning
  • Fewer challenges, more efficient in regulating learning

Optimizing Collaborative Learning Environments

This study demonstrates that a well-designed LMS can effectively scaffold learning regulation, especially in the initial phases of collaborative, phenomenon-based learning. While initial challenges in understanding tasks and group dynamics are common, strategic use of digital scaffolds, like checklist assignments, can significantly enhance shared metacognitive monitoring. Future designs should integrate dynamic LA feedback and AI-based tools to provide adaptive, real-time support, ensuring sustained engagement and more effective collaborative outcomes for primary students.

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