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This Work in Progress (WIP) paper will present an interview protocol development that leverages social media analysis for capturing narratives of neurodivergent (e.g., ADHD, autistic, dyslexic) engineering students. The work presented in this WIP is part of a larger mixed-methods sequential research project which aims to capture neurodivergent engineering student narratives that describe their engineering experiences in terms of strengths and challenges. Through social media analysis, we identified key language used by the neurodivergent community (e.g., neurodivergent, spoon, forget). We developed a few initial themes such as multiple pathways to recognizing one’s identity of being neurodivergent, multiple ways in which neurodivergence symptoms are experienced, and the ways in which an individual internally and outwardly interacts with their own symptoms. To capture neurodivergent narratives, we plan on conducting semi-structured interviews with neurodivergent engineering students three times over the semester (beginning, middle, end). Initial interview protocols for each interview will be developed and adjusted throughout the interview data collection process. An initial compilation of relevant interview questions was compiled from previous research and from the objectives of the research study. This initial pool of questions will then be refined based on our thematic findings and using the nuanced language identified on the social media platforms TikTok, Reddit, and Twitter. Results of this work will be presented in this paper as an interview protocol that will continue to be adapted as part of this larger research study, but can also be used as a starting point for researchers exploring similar topics in capturing the experience of neurodivergent engineering students. 

This Work in Progress (WIP) paper will present an interview protocol development that leverages social media analysis for capturing narratives of neurodivergent (e.g., ADHD, autistic, dyslexic) engineering students. The work presented in this WIP is part of a larger mixed-methods sequential research project which aims to capture neurodivergent engineering student narratives that describe their engineering experiences in terms of strengths and challenges. Through social media analysis, we identified key language used by the neurodivergent community (e.g., neurodivergent, spoon, forget). We developed a few initial themes such as multiple pathways to recognizing one’s identity of being neurodivergent, multiple ways in which neurodivergence symptoms are experienced, and the ways in which an individual internally and outwardly interacts with their own symptoms. To capture neurodivergent narratives, we plan on conducting semi-structured interviews with neurodivergent engineering students three times over the semester (beginning, middle, end). Initial interview protocols for each interview will be developed and adjusted throughout the interview data collection process. An initial compilation of relevant interview questions was compiled from previous research and from the objectives of the research study. This initial pool of questions will then be refined based on our thematic findings and using the nuanced language identified on the social media platforms TikTok, Reddit, and Twitter. Results of this work will be presented in this paper as an interview protocol that will continue to be adapted as part of this larger research study, but can also be used as a starting point for researchers exploring similar topics in capturing the experience of neurodivergent engineering students. 

A controlled vocabulary list that was originally developed for the automotive assembly environment was modified for home appliance assembly in this study. After surveying over 700 assembly tasks with the original vocabulary, additions were made to the vocabulary list as necessary. The vocabulary allowed for the transformation of work instructions in approximately 90% of cases, with the most discrepancies occurring during the inspection phase of the transfer line. The modified vocabulary list was then tested for coder reliability to ensure broad usability and was found to have Cohen’s kappa values of 0.671 < κ < 0.848 between coders and kappa values of 0.731 < κ < 0.875 within coders over time. Using this analysis, it was demonstrated that this original automotive vocabulary could be applied to the non-automotive context with a high degree of reliability and consistency.