Emergent Coding and Topic Modeling: A Comparison of Two Qualitative Analysis Methods on Teacher Focus Group Data

Miyaoka, Atsushi; Decker-Woodrow, Lauren; Hartman, Nancy; Booker, Barbara; Ottmar, Erin

doi:10.1177/16094069231165950

Cited by 3 publications

(2 citation statements)

References 21 publications

(31 reference statements)

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“…Following the qualitative thematic analysis, we also analyzed the interviewees’ responses quantitatively using topic modeling to identify latent topics as well as the most frequently used words. Quantitative text analysis has been found to be a useful tool for validating results of previous qualitative analysis [ 47 - 49 ]. In this case, we first removed the introductory and closing parts of the interviews that only contained introductions and small talk.…”

Section: Methodsmentioning

confidence: 99%

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Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study

Hummelsberger¹,

Koch²,

Rauh³

et al. 2023

JMIR AI

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Background Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. Objective This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. Methods For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. Results Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology’s performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. Conclusions Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.

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Section: Methodsmentioning

confidence: 99%

“…All text data processing and statistical analyses were performed using the statistical software R (version 4.1.1; R Foundation for Statistical Computing). Specifically, we used the R packages udpipe for tokenization [ 52 ] and topicmodels as well as ldatuning for topic modeling [ 49 , 53 ].…”

Section: Methodsmentioning

confidence: 99%

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study

Hummelsberger¹,

Koch²,

Rauh³

et al. 2023

JMIR AI

View full text Add to dashboard Cite

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Applying Personal Strengths: Building Well-Being and Resilience Strategies in an Undergraduate Wellness Course

Latimer,

Realff,

Templin

et al.

2024 ASEE Annual Conference &Amp; Exposition Proceedings

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is a second-year undergraduate student pursuing a degree in Industrial and Systems Engineering at Georgia Tech in Atlanta, Georgia. Currently, she serves as a student assistant for the Effective Team Dynamics Initiative, where she conducts engineering education research and facilitates strengths-based team dynamics workshops for first-year and transfer student seminars. Julianne is deeply passionate about promoting educational equity and K-12 STEM outreach. She aspires to further her studies in educational research and evaluation or instructional design in graduate school to continue making a positive impact in these areas.

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Learning from (re)experience: What mobile eye-tracking video can help us learn about the cognitive processes of teaching

Miller,

Correa,

Cortina

et al. 2024

Front. Educ.

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IntroductionClassroom teachers need to monitor a group of students varying in interest, knowledge, and behavior at the same time that they present a lesson and adapt it on the fly to student questions and understanding. Many areas of expertise are associated with special kinds of perceptual skills, and teaching presents its own perceptual challenges. We discuss the special nature of the expert looking that teachers must develop and how it relates to more general models of expertise. Standard methods of classroom video are limited in their support of teacher professional looking, and we explore an alternative using mobile eyetracking that overcomes many of these limits. The combination of mobile eyetracking records and standard video enables the participant to “re-experience” a situation in a vivid way, while also seeing things they missed the first time through.MethodsWe report a study in which pairs of novice and experienced teachers teaching the same students watched their own mobile eyetracking recordings while performing a retrospective think-aloud task.ResultsExperienced teachers were better able to describe high-level features and their significance in the lessons, while novices were more likely to talk about in-the-moment events such as things they failed to see while teaching. This is consistent with work on expertise that suggests there are both costs and benefits to expert looking.DiscussionOur results suggest that the ability to quickly grasp the meaning of a classroom situation may be associated with less awareness of some of the lower-level features on which those inferences are based. Novice and experienced teachers notice different things and have different perspectives on classroom processes; understanding the cognitive process of teachers will require combining insights from each. The methods used in this study are quickly becoming less costly and more accessible, and they have a unique role to play in research and in teacher professional development.

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Emergent Coding and Topic Modeling: A Comparison of Two Qualitative Analysis Methods on Teacher Focus Group Data

Cited by 3 publications

References 21 publications

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study

Applying Personal Strengths: Building Well-Being and Resilience Strategies in an Undergraduate Wellness Course

Learning from (re)experience: What mobile eye-tracking video can help us learn about the cognitive processes of teaching

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