Epistemic Network Analysis Visualization

Tan, Yuanru; Hinojosa, Cesar; Marquart, Cody; Ruis, Andrew R.; Shaffer, David Williamson

doi:10.1007/978-3-030-93859-8_9

Cited by 8 publications

(9 citation statements)

References 10 publications

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“…The present study offers a methodology and analysis to bridge established learning constructs from tutor log data (e.g., hint use and attempts in AI tutors) with teacher spatial data and teacher practice gleaned from observation codes. Specifically, we leverage the emergent methodology of Transmodal Analysis (TMA) [36] and Ordered Network Analysis (ONA) [38], further described in Section 3.4.1.…”

Section: Quantitative Ethnography Methods In Learning Analyticsmentioning

confidence: 99%

“…To better understand teacher events with different network ties to student actions across students with low and high learning rates, we employ log data replays inspired by prior work on labeling tutor log data [32]. Specifically, we sample a context window of three teacher actions (omitting observation notes unrelated to the teacher; see Section behavioral codes [38]. ONA constructs a sliding window for each observation and calculates connection counts between any two codes within it.…”

Section: Qualitative Text Replay Analysismentioning

confidence: 99%

“…The present study leverages network methods to fill this gap in understanding the interplay of in-tutor learning and classroom practices. Epistemic Network Analysis (ENA) [35] and Ordered Network Analysis (ONA) [38] in quantitative ethnography (QE) [35] receive increasing attention in learning analytics and promise interpretable insight into learning processes [34]. These methods encode temporal relationships between virtual and physical events and establish ties of which behaviors frequently co-occur (e.g., student disengagement and teacher monitoring).…”

mentioning

confidence: 99%

“…This research presents a case study in understanding classroom practices in a typical application area of AI-based learning systems: classrooms with individualized problemsolving practice. We combine Transmodal Analysis [36] and ONA [38] to study learning differences in the linear equation-solving tutor Lynnette [28] where our past work found teacher practices related to student learning [23].…”

mentioning

confidence: 99%

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Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis

Borchers,

Wang,

Karumbaiah

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher interactions. One key challenge in leveraging such data is generating meaningful insights into effective teacher practices. Quantitative ethnography bears the potential to close this gap by combining multimodal data streams into networks of co-occurring behavior that drive insight into favorable learning conditions. The present study uses transmodal ordered network analysis to understand effective teacher practices in relationship to traditional metrics of in-system learning in a mathematics classroom working with AI tutors. Incorporating teacher practices captured by position tracking and human observation codes into modeling significantly improved the inference of how efficiently students improved in the AI tutor beyond a model with tutor log data features only. Comparing teacher practices by student learning rates, we find that students with low learning rates exhibited more hint use after monitoring. However, after an extended visit, students with low learning rates showed learning behavior similar to their high learning rate peers, achieving repeated correct attempts in the tutor. Observation notes suggest conceptual and procedural support differences can help explain visit effectiveness. Taken together, offering early conceptual support to students with low learning rates could make classroom practice with AI tutors more effective. This study advances the scientific understanding of effective teacher practice in classrooms learning with AI tutors and methodologies to make such practices visible.

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Section: Quantitative Ethnography Methods In Learning Analyticsmentioning

confidence: 99%

Section: Qualitative Text Replay Analysismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis

Borchers,

Wang,

Karumbaiah

et al. 2024

Proceedings of the 14th Learning Analytics and Knowledge Conference

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“…For instance, in ENA maps, the coordinates have meaning, but our method is similar to traditional SNA maps in which the location of nodes have no meaning beyond proximity and connections to other nodes. Our networks are not weighted, and a larger number of nodes is desirable, while in ENA the networks are weighted and a smaller number of nodes is usually desirable (Tan et al , 2022). Therefore, we see our method as a synergistic companion to other analytical approaches in DBR such as ENA rather than an alternative.…”

Section: Introductionmentioning

confidence: 99%

Learning experience network analysis for design-based research

Donaldson,

Han,

Yan

et al. 2023

ILS

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Purpose Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis. Design/methodology/approach This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory. Findings The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations. Research limitations/implications LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning. Practical implications LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data. Originality/value To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

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