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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. 

A key goal of quantitative ethnographic (QE) models, and statistical models more generally, is to produce the most parsimonious model that adequately explains or predicts the phenomenon of interest. In epistemic network analysis (ENA), for example, this entails constructing network models with the fewest number of codes whose interaction structure provides sufficient explanatory power in a given context. Unlike most statistical models, however, modification of ENA models can affect not only the statistical properties but also the interpretive alignment between quantitative features and qualitative meaning that is a central goal in QE analyses. In this study, we propose a novel method, Parsimonious Removal with Interpretive Alignment, for systematically identifying more parsimonious ENA models that are likely to maintain interpretive alignment with an existing model. To test the efficacy of the method, we implemented it on a well-studied dataset for which there is a published, validated ENA model, and we show that the method successfully identifies reduced models likely to maintain explanatory power and interpretive alignment.