Purpose The goal of this study is to explore an immediate step in understanding the lived experiences of under-represented students through metaphor construction and possibly collect more in-depth data through photograph-based interviews. Design/Methodology/Approach This article introduced photo-elicitation based narrative interviews as a qualitative methodology while interviewing fourteen undergraduate community college students mostly from underrepresented groups (URGs). At the beginning of each interview, the authors probed the participants with 8 photographs chosen by the research team to represent a diverse set of experiences in engineering. The authors conducted a thematic analysis of the interview data. Findings The findings suggested that the inclusion of photo-elicitation often catalyzed consumption of representations, images, metaphors, and voice to stories passed unnoticed; and finally produces more detailed descriptions and complements semi-structured narrative interviews. Research Limitations/Implications This study advances the scholarship that extends photograph driven interviews/photo elicitation methodology while interviewing marginalized population and offers a roadmap for what a multi-modal, arts-based analysis process might look like for in-depth interviews. Practical Implications The use of photo-elicitation in our research enabled a deeper, more poignant exploration of the URG students' experience of navigating engineering. The participants were able to relate to the photographs and shared their life narratives through them; hence, use of photographs can be adapted in future research. Social Implications Our research revealed that PEI has excellent potential to capture marginalized narratives of URGs, which is not well explored in educational research, specially, in higher education. In our research, PEI promoted more culturally inclusive approaches positioning the participants as experts of their own narratives. Originality/Value The study presented in this paper serves as an example of qualitative research that expands methodological boundaries and centers the role of power, marginalization, and creativity in research. This work serves as a unique and important contribution to the photo-elicitation literature, offering a critical roadmap for researchers who are drawn to photo elicitation/photograph driven interviews as a method to explore their inquiry.
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Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives
Narratives are fundamental to our perception of the world and are pervasive in all activities that involve the representation of events in time. Yet, modern online information systems do not incorporate narratives in their representation of events occurring over time. This article aims to bridge this gap, combining the theory of narrative representations with the data from modern online systems. We make three key contributions: a theory-driven computational representation of narratives, a novel extraction algorithm to obtain these representations from data, and an evaluation of our approach. In particular, given the effectiveness of visual metaphors, we employ a route map metaphor to design a narrative map representation. The narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map. Each element of our representation is backed by a corresponding element from formal narrative theory, thus providing a solid theoretical background to our method. Our approach extracts the underlying graph structure of the narrative map using a novel optimization technique focused on maximizing coherence while respecting structural and coverage constraints. We showcase the effectiveness of our approach by performing a user evaluation to assess the quality of the representation, metaphor, and visualization. Evaluation results indicate that the Narrative Map representation is a powerful method to communicate complex narratives to individuals. Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.
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- Award ID(s):
- 2128642
- NSF-PAR ID:
- 10334913
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 4
- Issue:
- CSCW3
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 33
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3432927
