Socially Shared Regulation of Learning

• Socially Shared Regulation of Learning (SSRL) is a framework that defines how individual self-regulation is distributed into collective regulation through joint planning, monitoring, and reflection.
• It integrates theories of self- and co-regulated learning with multimodal analytics to assess synchronized cognitive, metacognitive, emotional, and motivational exchanges.
• SSRL has practical applications in education and medicine, leveraging mobile technologies and AI to enhance group collaboration and improve learning outcomes.

Socially Shared Regulation of Learning (SSRL) denotes a theoretical and empirical framework explicating how regulation of learning becomes distributed, negotiated, and operationalized within groups, transforming isolated self-regulation processes into coordinated collective action. SSRL is characterized by mutual goal setting, joint monitoring, and collaborative reflection, underpinned by synchronous cognitive, metacognitive, emotional, and motivational exchanges among learners. This construct is critical for understanding effective collaboration in educational and professional contexts, particularly in environments featuring complexity, ambiguity, or distributed expertise.

1. Theoretical Foundations of SSRL

Socially Shared Regulation of Learning emerges from the intersection of individual self-regulated learning (SRL), co-regulated learning (where individuals scaffold one another), and group-level processes. Foundational models referenced in SSRL research include Zimmerman's cyclical SRL model, which delineates phases of forethought (goal setting and planning), performance (strategy execution and monitoring), and self-reflection (evaluation and adaptation):

$$\text{SRL Cycle} = f(\text{Forethought}, \text{Performance}, \text{Reflection})$$

In SSRL, these phases become interdependent such that regulation is not only enacted individually but also collaboratively—learners share responsibility for all phases, producing a networked cycle of joint regulation. Oxford’s Strategic Self-Regulation (S2R) framework further typifies regulation as a multi-level system involving metastrategies, strategies, and tactics, operationalized within collective tasks and interactions (Viberg et al., 2021).

Four canonical SSRL dimensions are established:

• Metacognitive: Joint planning, self-monitoring, and sharing evaluative feedback.
• Cognitive: Shared sense-making, problem-solving, and co-construction of knowledge.
• Socio-emotional: Group-level management of affect, cohesion, and relational trust.
• Socio-motivational: Sustaining engagement and collective goal alignment.

This structure is commonly formalized as:

$$SSRL_{\text{total}} = \sum_{i \in \{\text{meta}, \text{cog}, \text{emo}, \text{mot}\}} w_{i} \times S_{i}$$

where $S_i$ corresponds to the quantified interaction level and $w_i$ assigns importance (Huang et al., 2 May 2025).

2. Empirical Methodologies and Multimodal Analytics

Contemporary SSRL research employs multimodal analytics to dissect regulation in collaborative contexts. Experimental paradigms integrate intentional triggers (cognitive and emotional) to induce regulatory episodes, captured across synchronized video (360° and facial), Kinect-based gesture data, audio, and high-frequency physiological signals (EDA, heart rate, accelerometry) (Li et al., 2022).

Advanced emotion recognition tools (EmoNet, Emotion-GCN) extract facial affective states, which are algorithmically mapped to positive/negative/neutral valences and statistically associated with regulatory events via $\chi^2$ and Spearman's $\rho$ analyses. Gesture synchronization and alignment of physiological arousal permit quantification of socio-emotional co-regulation within groups. Findings indicate that externally induced triggers increase group-level positive emotions by approximately 25% and foster synchronization in both emotional and behavioral dimensions (e.g., movement speed and gesture alignment)—central indicators of SSRL emergence (Li et al., 2022).

Transmodal Analysis (TMA) further fuses multimodal codes within temporally-defined windows, enabling subtraction of regulatory networks between experts and novices. These methods have elucidated that expert groups manifest tightly-coupled socio-cognitive/high-arousal emotion networks (e.g., surprise, anger), while novices show fragmented patterns, indicating differential SSRL proficiency (Huang et al., 18 Oct 2025).

3. SSRL in Mobile, Situated, and Technology-Mediated Environments

Mobile technology, learning analytics, and artificial intelligence are increasingly leveraged to support and analyze SSRL. Mobile-Assisted Language Learning (MALL) environments facilitate anytime/anywhere access, provide self-monitoring (e.g., TimeTracker app), and deliver real-time dashboards. When coupled with collaborative activities (instant messaging, joint artifacts), these tools enable distributed regulation of learning, shifting group learning processes from mere co-regulation to shared regulation (Viberg et al., 2021).

Empirical studies demonstrate that mobile SRL mechanisms (goal-setting, progress visualization) can significantly improve individual motivation and group performance. When these tools are integrated with AI-driven feedback (e.g., personalized scaffolding, behavior analysis), learners receive adaptive guidance that enhances both individual self-regulation and SSRL dynamics. These findings indicate that the infrastructural affordances of mobile and analytic technologies are instrumental in promoting SSRL in out-of-class, diverse learning settings.

4. SSRL Frameworks and Innovations

Recent research has advanced SSRL theory through refined frameworks and models. The Emergent Explicit Regulation (EER) framework formalizes the in-the-moment, observable regulatory moves enacted by group members in response to task challenges. EER decomposes regulation into emergent context, explicitness (via speech, gesture, or signing), challenge trigger, regulatory action, tangible effects, and target areas (cognitive, behavioral, motivational, emotional, and social) (Cao et al., 13 Aug 2025):

$$EER = \{ \text{Emergent}, \text{Explicit}, \text{Challenge}, \text{Regulation}, \text{Effects}, T \}$$

where $$T = \{ \text{cognition}, \text{behavior}, \text{motivation}, \text{emotion}, \text{social} \}$$.

Case studies reveal that EER captures spontaneous regulatory (e.g., “jumpstart”) moves that are crucial for effective SSRL, especially in inquiry-driven and ambiguous tasks. This framework enables granular analysis and informs task design, scaffolding, and role adaptation in collaborative scientific learning.

The InCoRe model (Interactive Co-Regulation) integrates psychological diagnostics (OPD), computational affect models (ALMA), social norms frameworks (Geni:OS), and virtual simulation technologies to automate and empirically annotate teacher-student co-regulation within immersive VR training systems (Bhuvaneshwara et al., 27 Feb 2025). InCoRe operationalizes SSRL at the intersection of subjective emotional experiences and observed classroom management behaviors, using annotation schemes grounded in psychodynamic theory and empirical ratings (e.g., $\chi^2(70)=108.96, p=0.002, V=0.58$).

5. SSRL Applications in Medical Education and Simulation

SSRL is particularly salient in medical education, where collaborative diagnostic reasoning demands high-fidelity socio-cognitive, emotional, and motivational management. SSRLBot is an LLM-based agent developed to analyze and scaffold SSRL in medical teams during diagnostic tasks. The agent processes conversational data, categorizes SSRL dimensions per participant, and provides actionable recommendations for improvement. Empirical evaluation against other LLM models reveals that SSRLBot delivers more nuanced, theory-aligned, and individualized feedback, explicitly linking observed behaviors to SSRL dimensions and fostering enhanced teamwork (Huang et al., 2 May 2025).

Additional research employing facial-emotion recognition (FaceReader) in medical simulation contexts demonstrates that SSRL is manifested differently by experts and novices. Experts display coherent SSRL strategies coupled with high-arousal emotions, indicative of focused engagement. Novices exhibit less structured SSRL patterns, with frequent transitions between happiness and sadness, possibly reflecting distraction or cognitive overload—thereby elucidating the necessity for tailored scaffolding and adaptive support to promote robust SSRL among less experienced learners (Huang et al., 18 Oct 2025).

6. Practical and Pedagogical Implications

The SSRL construct informs diagnostic tools, instructional design, and adaptive scaffolding strategies. Educators can leverage SSRL analytics to detect regulatory moments, provide calibrated prompts, and design inquiry tasks that elicit explicit group regulation. Hybrid AI systems merging psychological, sociological, and affective models in virtual environments enable safe training and reflection for socioemotional classroom management (Bhuvaneshwara et al., 27 Feb 2025). SSRL frameworks advocate for distributed leadership roles and flexible regulation in collaborative settings, expanding applicability to multicultural, multidisciplinary, and modality-rich environments.

A plausible implication is that the growing integration of SSRL frameworks with multimodal, AI-driven analytics will continue to transform collaborative learning and decision-making in high-stakes domains, supporting adaptive, data-driven optimization of collective regulation strategies across contexts.

7. Challenges, Limitations, and Future Directions

Key challenges include accurately capturing and sustaining SSRL in diverse, “in-the-wild” settings; synchronizing multimodal data streams for real-time regulatory feedback; and balancing individual and collective regulation in heterogeneous groups. Future research directions entail refinement of SSRL mapping, integration of expanded multimodal features (physiological, gesture, discourse), and iterative development of adaptive support tools (e.g., for novice teams in medical and scientific domains) (Cao et al., 13 Aug 2025, Huang et al., 18 Oct 2025). Ongoing work aims to harness user-feedback mechanisms, optimize weighting schemes for SSRL dimensions, and extend SSRLBot-like agents to broader collaborative environments.

In summary, Socially Shared Regulation of Learning represents a critical paradigm for understanding and fostering collective regulation in group learning and work, with empirical, theoretical, and technological advancements driving its application across educational, medical, and professional sectors.