Trajectories in Epistemic Network Analysis

Brohinsky, Jais; Marquart, Cody; Wang, Junting; Ruis, Andrew R.; Shaffer, David Williamson

doi:10.1007/978-3-030-67788-6_8

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Prior to dimensional reduction, the ENA model normalized the networks for all units of analysis. Networks were aggregated to account for the fact that some people think aloud more frequently than others (Brohinsky et al, 2021). A singular value decomposition (SVD) was used to reduce the dimensions, which results in orthogonal dimensions that maximize the variance explained by each dimension (see Shaffer et al, 2016 for a more detailed explanation of the mathematics).…”

Section: Discussionmentioning

confidence: 99%

Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis

Huang

Wang

et al. 2023

Computer Assisted Learning

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BackgroundMedical students use a variety of self‐regulated learning (SRL) strategies in different medical reasoning (MR) processes to solve patient cases of varying complexity. However, the interplay between SRL and MR processes is still unclear.ObjectivesThis study investigates how self‐regulated learning (SRL) and medical reasoning (MR) occurred concurrently in medical students while completing a diagnostic task in an intelligent tutoring system. This study aims to provide new insights into performance differences between high‐ and low‐achieving students in tasks of varying complexity.MethodsThirty‐one medical students (67.6% female) from a large North American university were tasked with solving two virtual patient cases in an intelligent tutoring system, BioWorld. BioWorld was designed for medical students to practice clinical reasoning skills deliberately. We collected students' think‐aloud protocols, based on which we coded their use of SRL behaviours and medical reasoning activities. We analysed the co‐occurrences of SRL behaviours and medical reasoning activities using the epistemic network analysis (ENA) method.ResultsThe SRL behaviour self‐reflection and MR activity lines of reasoning co‐occurred more frequently in a difficult task than in an easy task. In both tasks, high performers demonstrated more co‐occurrences of self‐reflection and lines of reasoning than low performers. Moreover, the MR activity conceptual operations co‐occurred more frequently with the SRL activities of monitoring and evaluation among high performers compared to low performers in an easy task.ImplicationsThe co‐occurrences of SRL behaviours and MR processes account for students' performance differences. The design of computer‐based learning environments for clinical reasoning should promote the acquisition of both SRL and medical reasoning abilities. Moreover, medical educators should consider task complexity when scaffolding.

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Section: Discussionmentioning

confidence: 99%

Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis

Huang

Wang

et al. 2023

Computer Assisted Learning

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“…The two interpretable axes can be separated and ordered by patch (or e.g., time spent on patch), making possible the representation of trajectories across both ENA dimensions. In a trajectory model, every ENA score is interpreted by three variables: its x-value, its y-value, and whatever variable is defining the trajectories (e.g., patch, time, duration) 28 . Visualizations are co-registered, that is, the mathematical properties of a model are aligned with information in associated visualizations.…”

Section: Methodsmentioning

confidence: 99%

A feasibility study for a unified, multimodal analysis of online information foraging in health-related topics

Zörgő,

Peters,

Jeney

et al. 2024

Open Res Europe

Self Cite

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Background Digital health literacy (DHL) is the ability to find, understand, and appraise online health-related information, as well as apply it to health behavior. It has become a core competence for navigating online information and health service environments. DHL involves solving ill-structured problems, where the problem and its solution are not clearcut and may have no single answer, such as in the process of sensemaking. We employ and expand on information foraging theory to address how experts and novices in information retrieval perform a search task. Our overarching aim is to pinpoint best practices and pitfalls in understanding and appraising health-related information online to develop a digital intervention to increase DHL and critical thinking. Methods In this feasibility study, we recruited a total of twenty participants for our expert and novice subsamples. We collected sociodemographic data with a self-developed survey, video data through an observation protocol of a 10-minute search task, as well as audio-video data via a retrospective think-aloud. The three, multimodal data streams were transcribed and aligned. Codes were developed inductively in several iterations, then applied deductively to the entire dataset. Tabularized, coded and segmented qualitative data were used to create various quantitative models, which demonstrate viability for the qualitative and statistical comparison of our two subsamples. Results Data were visualized with Epistemic Network Analysis to analyze code co-occurrences in the three aligned data streams, and with Qualitative/Unified Exploration of State Transitions to examine the order in which participants in our two subsamples encountered online content. Conclusions This paper describes our methods and planned analyses elaborated with mock figures. Quantifying qualitative data, aligning data streams, and representing all information in a tabularized dataset allows us to group data according to various participant attributes and employ data visualization techniques to pinpoint patterns therein.

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“…: [21]). While relying on codes to define dimensions, searching for the most distal nodes along an axis will generally be most appropriate (e.g., [26]) because those signify the most dissimilar co-occurrence patterns within a single dimension. Even relatively small nodes with weaker connections, but located at the far end of an axis, may drive interpretation on a particular dimension, albeit not be a salient contributor to the model as a whole [14].…”

Section: The Projection Spacementioning

confidence: 99%

Making Sense of the Model: Interpreting Epistemic Networks and their Projection Space

Zörgő,

Árva,

Eagan

2024

Preprint

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Unified methods enable iteration and dialogue between qualitative data and their quantified form; Epistemic Network Analysis (ENA) is a common tool for modeling such data. Little guidance is available on how to interpret ENA models; we aim to contribute to filling this lacuna. We present approaches to interpreting ENA graphs (node size, connection strength, node proximity, etc.) and the space into which they are projected (plotted point locations, dimensions, etc.). These approaches are then elaborated with an example from an empirical study and contextualized via discussing their limitations and implications.

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Trajectories in Epistemic Network Analysis

Cited by 16 publications

References 13 publications

Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis

Exploring the co‐occurrence of students' learning behaviours and reasoning processes in an intelligent tutoring system: An epistemic network analysis

A feasibility study for a unified, multimodal analysis of online information foraging in health-related topics

Making Sense of the Model: Interpreting Epistemic Networks and their Projection Space

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