Deliberation as a micro-level lens for operationalizing socially shared regulation of learning¶

Theory · All theories
Evidence · 6 claims (6 for) · 1 study (1 associational), q2 · 1 of 1 report an effect size · 6 claims rest on one study

Description¶

The article proposes deliberation — the "deliberative, negotiation characteristic of interactions for regulation in CL contexts" — as a new micro-level theoretical lens for SSRL. Unlike negotiation, which resolves conflicts through compromise, deliberation emphasizes "discussing and evaluating diverse perspectives to reach a well-informed, thoughtful decision." Eleven micro-level deliberative processes were identified inductively (e.g., define the problem, generate options, establish strategy, agree and implement, attempt ideas, monitoring, evaluating, educate each other), aligned with SSRL theory. This lens makes SSRL's abstract regulatory processes quantifiable at the level of individual utterance turns, enabling AI-driven sequence, network and process analyses.

Design Implications¶

Context¶

Requirements¶

• Requires fine-grained transcription and coding of utterance-level discourse data with a context window of 7–10 turns
• Requires a validated deliberative characteristics coding scheme (κ = 0.76 in this study)

Constraints¶

• Developed and validated in a controlled laboratory setting with a single collaborative task type
• Coding was resource-intensive despite AI-assisted transcription

Target Learners¶

• secondary school students in collaborative learning

Target Learning Objectives¶

• socially shared regulation of learning
• collaborative problem-solving and adaptation

Claims¶

• Pia Tfa Deliberation Patterns Ssrl [+M]
• Metacognitive Alone Not Effective Regulation [+M]
• PIA and TFA types differ structurally in deliberative networks, with PIA centred on establishing strategy, educating, agreeing and monitoring [+M]
• Most groups maintain stable deliberation patterns across triggering events and fail to adapt to emerging regulatory needs [+M]
• PIA groups follow a strategic generate-options-to-implement cycle with fewer random idea attempts, while TFA groups loop through repeated metacognitive interactions and unproductive idea testing [+M]
• PIA and TFA clusters differ significantly in both deliberative and regulatory interaction proportions [+W]

Related Theories¶

Examples¶

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Key Sources¶

• Belle Dang, Andy Nguyen and Sanna Järvelä. (2024). Deliberative Interactions for Socially Shared Regulation in Collaborative Learning: An AI-Driven Learning Analytics Study. Journal of Learning Analytics, 11(3). https://doi.org/10.18608/jla.2024.8393