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Self-regulated learning (SRL) is the ability to regulate cognitive, metacognitive, motivational, and emotional states while learning and is posited to be a strong predictor of academic success. It is therefore important to provide learners with effective instructions to promote more meaningful and effective SRL processes. One way to implement SRL instructions is through providing real-time SRL scaffolding while learners engage with a task. However, previous studies have tended to focus on fixed scaffolding rather than adaptive scaffolding that is tailored to student actions. Studies that have investigated adaptive scaffolding have not adequately distinguished between the effects of adaptive and fixed scaffolding compared to a control condition. Moreover, previous studies have tended to investigate the effects of scaffolding at the task level rather than shorter time segments—obscuring the impact of individual scaffolds on SRL processes. To address these gaps, we (a) collected trace data about student activities while working on a multi-source writing task and (b) analyzed these data using a cutting-edge learning analytic technique— ordered network analysis (ONA)—to model, visualize, and explain how learners' SRL processes changed in relation to the scaffolds. At the task level, our results suggest that learners who received adaptive scaffolding have significantly different patterns of SRL processes compared to the fixed scaffolding and control conditions. While not significantly different, our results at the task segment level suggest that adaptive scaffolding is associated with earlier engagement in SRL processes. At both the task level and task segment level, those who received adaptive scaffolding, compared to the other conditions, exhibited more task-guided learning processes such as referring to task instructions and rubrics in relation to their reading and writing. This study not only deepens our understanding of the effects of scaffolding at different levels of analysis but also demonstrates the use of a contemporary learning analytic technique for evaluating the effects of different kinds of scaffolding on learners' SRL processes.
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This content will become publicly available on August 3, 2026
Self-Reflective Crowds: Surfacing Wisdom through Emergent Scaffolding
Producing open-ended creative work through crowdsourcing remains a challenge, as workers often lack domain expertise, and requesters struggle to provide scalable guidance. Can the workers themselves create materials that guide subsequent workers? In this paper, we prototype a workflow for emergent scaffolding, where hints, rubrics, and examples are generated by crowd workers after attempting the task. We demonstrate how an iterative Train-Try-Reflect-Synthesize pattern—supported by LLMs—can produce a structured rubric with graded examples to guide subsequent workers on a task to create digital illustrations for scientific papers. To evaluate this strategy, we conducted a between-subjects experiment with three conditions: baseline instructions, generic examples, and emergent scaffolding. Participants in the emergent scaffolding condition created significantly better illustrations, as rated by blind-to-condition judges, compared to generic examples or instructions only. While self-efficacy ratings were mixed across conditions, emergent scaffolding participants provided better feedback during their post-task reflections. We discuss the potential for emergent scaffolding to support and scale up complex, creative tasks in crowdwork.
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- Award ID(s):
- 2009003
- PAR ID:
- 10654065
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 169 to 187
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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