Sense-making Strategies for the Interpretation of Visualizations—Bridging the Gap between Theory and Empirical Research

Pohl, Margit; Haider, Johanna Doppler

doi:10.3390/mti1030016

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Another reason students struggle with the job inside the realm of language is the absence of a second representation register for the prompt and a common name for the theorem. The geometric symbol enables simultaneous processing of symbols and words, which is required for comprehending geometric concepts and mathematical sense-making (Pohl and Doppler Haider, 2017). The geometrical version of the theorem can assist in comprehending the operation required for the issue-posing assignment.…”

Section: Discussionmentioning

confidence: 99%

Students’ understanding of a geometric theorem: A case of grade 9 problem posing

Patac

Patac²,

Crispo³

2022

J. Res. Adv. Math. Educ

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Teaching axiomatic representation of mathematical objects in all grades can and should be done. The paper analyzes students' understanding and how they perceive theorems using problem posing. We looked at how English-language learners create questions about four geometric theorems from a 9th-grade math textbook. The analysis looks at the question's distinctiveness, its elements' relationships, and sentence structure flaws. These lines, angle, and triangle theorems were chosen to exemplify problem scenarios when a theorem is conveyed in words but not explicitly symbolized. The difficulty of posing mathematically relevant problems stems from the required process of simultaneously changing the theorem language, home language, and formal mathematics language. In Van Hiele's methodology, the pupils' issues aren't classified as a formal or informal deduction. Questions either deduce from a formal system or emphasize theorems. Mastering the required representation registers can assist students in posing problems that reflect, at the very least, at the formal deduction level. The absence of symbolic representation increases the difficulty in posing original problems involving geometric theorems. As a result, how problems are made, especially how they are written, shows how well students understand math through problem-posing.

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Section: Discussionmentioning

confidence: 99%

Students’ understanding of a geometric theorem: A case of grade 9 problem posing

Patac

Patac²,

Crispo³

2022

J. Res. Adv. Math. Educ

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show abstract

“…Heer et al [22] described how users interact with a visual analytics tool by evaluating aggregated collections of history sessions. Pohl et al [32] qualitatively analyzed interaction provenance based on thinkingaloud protocols. They identified various strategies that users applied to interpret and understand visualizations: comparing, laddering, explaining (storytelling), summarizing, eliminating, and verifying.…”

Section: Provenance Meta-analysismentioning

confidence: 99%

Provectories: Embedding-Based Analysis of Interaction Provenance Data

Walchshofer¹,

Hinterreiter²,

Xu³

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manual processes such as interviews and the analysis of observational data. While it is technically possible to capture a history of user interactions and application states, it remains difficult to extract and describe analysis strategies based on interaction provenance. In this paper, we propose a novel visual approach to the meta-analysis of interaction provenance. We capture single and multiple user sessions as graphs of high-dimensional application states. Our meta-analysis is based on two different types of two-dimensional embeddings of these high-dimensional states: layouts based on (i) topology and (ii) attribute similarity. We applied these visualization approaches to synthetic and real user provenance data captured in two user studies. From our visualizations, we were able to extract patterns for data types and analytical reasoning strategies.

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“…Heer et al [21] described how users interact with a visual analytics tool by evaluating aggregated collections of history sessions. Pohl et al [31] qualitatively analyzed interaction provenance based on thinking-aloud protocols. They identified various strategies that users applied to interpret and understand visualizations: comparing, laddering, explaining (storytelling), summarizing, eliminating, and verifying.…”

Section: Provenance Meta-analysismentioning

confidence: 99%

Provectories: Embedding-based Analysis of Interaction Provenance Data

Walchshofer¹,

Hinterreiter²,

Xu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manual processes such as interviews and the analysis of observational data. While it is technically possible to capture a history of user interactions and application states, it remains difficult to extract and describe analysis strategies based on interaction provenance. In this paper, we propose a novel visual approach to meta-analysis of interaction provenance. We capture single and multiple user sessions as graphs of high-dimensional application states. Our meta-analysis is based on two different types of two-dimensional embeddings of these high-dimensional states: layouts based on (i) topology and (ii) attribute similarity. We applied these visualization approaches to synthetic and real user provenance data. From our visualizations, we were able to extract patterns for data types and analytical reasoning strategies.

show abstract

Sense-making Strategies for the Interpretation of Visualizations—Bridging the Gap between Theory and Empirical Research

Cited by 5 publications

References 39 publications

Students’ understanding of a geometric theorem: A case of grade 9 problem posing

Students’ understanding of a geometric theorem: A case of grade 9 problem posing

Provectories: Embedding-Based Analysis of Interaction Provenance Data

Provectories: Embedding-based Analysis of Interaction Provenance Data

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