Designing an Interface for Sharing Quantitative Ethnographic Research Data

Swiecki, Zachari; Marquart, Cody; Sachar, Arjun; Hinojosa, Cesar; Ruis, Andrew R.; Shaffer, David Williamson

doi:10.1007/978-3-030-33232-7_30

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Relatedly, our ONA analysis only explored transitions between pairs of codes. Stronger differences may be observed for longer sequences, but this approach could reduce the interpretability of the results (Swiecki et al, 2019 ). Third, SRL is inherently contextual (Winne, 2010 ), and therefore, the research findings in the current study can only be referred to other similar learning tasks (i.e., read-and-write essay-writing tasks).…”

Section: Discussionmentioning

confidence: 99%

Analytics of self-regulated learning scaffolding: effects on learning processes

Fan

Tan

et al. 2023

Front. Psychol.

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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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Section: Discussionmentioning

confidence: 99%

Analytics of self-regulated learning scaffolding: effects on learning processes

Fan

Tan

et al. 2023

Front. Psychol.

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“…Moreover, as a way to extend the QE methodology to a larger audience, it is important for the QE community to attend to technical aspects such as attendability and interpretability since some of the tools and outputs from QE analysis currently require sophisticated mathematical reasoning and prior experience and training in both qualitative and statistical analysis [48]. One possible approach is to develop simple tools with an interface as those suggested by [49] and [50] that support the sharing of QE outputs with the broader public to encourage uptake of QE in other fields of practice that are inherently less quantitative. We believe that the strength of work from individual researchers reported in this review and initiatives such as the recently established International Society for Quantitative Ethnography (ISQE) can present a powerful force to shape the identity of the QE community.…”

Section: Implications and Conclusionmentioning

confidence: 99%

Scoping the Emerging Field of Quantitative Ethnography: Opportunities, Challenges and Future Directions

Kaliisa

Misiejuk

Irgens

et al. 2021

Communications in Computer and Information Science

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Quantitative Ethnography (QE) is an emerging methodological approach that combines ethnographic and statistical tools to analyze both Big Data and smaller data to study human behavior and interactions. This paper presents a methodological scoping review of 60 studies employing QE approaches with an intention to characterize and establish where the boundaries of QE might and should be in order to establish the identity of the field. The key finding is that QE researchers have enough commonality in their approach to the analysis of human behavior with a strong focus on grounded analysis, the validity of codes and consistency between quantitative models and qualitative analysis. Nonetheless, in order to reach a larger audience, the QE community should attend to a number of conceptual and methodological issues (e.g. interpretability). We believe that the strength of work from individual researchers reported in this review and initiatives such as the recently established International Society for Quantitative Ethnography (ISQE) can present a powerful force to shape the identity of the QE community.

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