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1. Examining the Literacy Practices of Engineers to Develop a Model of Disciplinary Literacy Instruction for K-12 Engineering (Work in Progress)

   Green, Theresa ; Minichiello, Angela ; Wilson-Lopez, Amy ( June 2018 , ASEE Annual Conference proceedings)

   Despite efforts to diversify the science, technology, engineering, and mathematics (STEM) workforce, engineering remains a White, male-dominated profession. Often, women and underrepresented students do not identify with STEM careers and many opt out of STEM pathways prior to entering high school or college. In order to broaden participation in engineering, new methods of engaging and retaining those who are traditionally underrepresented in engineering are needed. This work is based on a promising approach for encouraging and supporting diverse participation in engineering: disciplinary literacy instruction (DLI). Generally, teachers use DLI to provide K-12 students with a framework for interpreting, evaluating, and generating discipline-specific texts. This instruction provides students with an understanding of how experts in the discipline read, engage, and generate texts used to solve problems or communicate information. While models of disciplinary literacy have been developed and disseminated in several humanities and science fields, there is a lack of empirical and theoretical research that examines the use of DLI within the engineering domain. It is thought that DLI can be used to foster diverse student interest in engineering from a young age by removing literacy-based barriers that often discourage underrepresented students from entering and pursuing careers in STEM fields. This work-in-progress paper describes a new study underway to develop and disseminate a model of disciplinary literacy in engineering. During this project, researchers will observe, interview, and collect written artifacts from engineers working across four sub-disciplines of engineering: aerospace/mechanical, biological, civil/environmental, and electrical/computer. Data that will be collected include interview transcripts, observation field notes, engineer logs of literacy practices, and photographs of texts that the engineers read and write. Data will be analyzed using constant comparative analytic (CCA) methods. CCA will be used to generate theoretical codes from the data that will form the basis for a model of disciplinary literacy in engineering. As a primary outcome of this research, the engineering DLI model will promote the use of DLI practices within K-12 engineering instruction in order to assist and encourage diverse, underrepresented students to engage in engineering courses of study and pursue STEM careers. Thus far, the research team has begun collecting and analyzing data from two electrical engineers. This work in progress paper will report on preliminary findings, as well as implications for K-12 classroom instruction. For instance, this study has shed insights on how engineers use texts as part of the process of conducting failure analysis, and the research team has begun to conceptualize how these types of texts might be used with K-12 students to help them conduct failure analyses during design testing. Ultimately, this project will result in a list of grade-appropriate texts, evaluative frameworks, and activities (e.g., failure analysis in testing) that K-12 engineering teachers can use to prepare their diverse students to think, act, read, and write like engineers. 

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2. Developing a Model of Disciplinary Literacy Instruction for K-12 Engineering Education: Comparing the Literacy Practices of Electrical and Mechanical Engineers (Fundamental)

   Green, T. ; Wilson-Lopez, A. ; Minichiello, A. ( July 2019 , ASEE Annual Conference proceedings)

   Despite efforts to diversify the engineering workforce, the field remains dominated by White, male engineers. Research shows that underrepresented groups, including women and minorities, are less likely to identify and engage with scientific texts and literacy practices. Often, children of minority groups and/or working-class families do not receive the same kinds of exposure to science, technology, engineering, and mathematics (STEM) knowledge and practices as those from majority groups. Consequently, these children are less likely to engage in school subjects that provide pathways to engineering careers. Therefore, to mitigate the lack of diversity in engineering, new approaches able to broadly support engineering literacy are needed. One promising approach is disciplinary literacy instruction (DLI). DLI is a method for teaching students how advanced practitioners in a given field generate, interpret, and evaluate discipline-specific texts. DLI helps teachers provide access to to high quality, discipline-specific content to all students, regardless of race, ethnicity, gender, or socio-economic status, Therefore, DLI has potential to reduce literacy-based barriers that discourage underrepresented students from pursuing engineering careers. While models of DLI have been developed and implemented in history, science, and mathematics, little is known about DLI in engineering. The purpose of this research is to identify the authentic texts, practices, and evaluative frameworks employed by professional engineers to inform a model of DLI in engineering. While critiques of this approach may suggest that a DLI model will reflect the literacy practices of majority engineering groups, (i.e., White male engineers), we argue that a DLI model can directly empower diverse K-16 students to become engineers by instructing them in the normed knowledge and practices of engineering. This paper presents a comparative case study conducted to investigate the literacy practices of electrical and mechanical engineers. We scaffolded our research using situated learning theory and rhetorical genre studies and considered the engineering profession as a community of practice. We generated multiple types of data with four participants (i.e., two electrical and two mechanical engineers). Specifically, we generated qualitative data, including written field notes of engineer observations, interview transcripts, think-aloud protocols, and engineer logs of literacy practices. We used constant comparative analysis (CCA) coding techniques to examine how electrical and mechanical engineers read, wrote, and evaluated texts to identify the frameworks that guide their literacy practices. We then conducted within-group and cross-group constant comparative analyses (CCA) to compare and contrast the literacy practices specific to each sub-discipline Findings suggest that there are two types of engineering literacy practices: those that resonate across both mechanical and electrical engineering disciplines and those that are specific to each discipline. For example, both electrical and mechanical engineers used test procedures to review and assess steps taken to evaluate electrical or mechanical system performance. In contrast, engineers from the two sub-disciplines used different forms of representation when depicting components and arrangements of engineering systems. While practices that are common across sub-disciplines will inform a model of DLI in engineering for K-12 settings, discipline-specific practices can be used to develop and/or improve undergraduate engineering curricula. 

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3. Understanding the Situated Workplace Practices and Habits of Engineers Using Agile Ethnography

   Green, Theresa ; Minichiello, Angela ; Wilson-Lopez, Amy ( August 2022 , 2022 ASEE Annual Conference & Exposition)

   This methods paper describes the application of and insights gained from using aspects of an emerging methodology, agile ethnography, to study engineers working in practice. Research has suggested that there is a misalignment between what is taught in engineering school and the types of work that engineers do in practice [1]. Little is known about the types of engineering work that are conducted in practice [2], [3]. In order to best prepare engineering graduates to meet the demands of the engineering workforce, students should be taught the types of knowledge and problem-solving strategies that are commonly used by practicing engineers. By teaching students the problem-solving strategies that are used by their professional counterparts, the gap between what students are taught in school and what is expected of them in the workplace may be lessened. The purpose of this paper is to describe how agile ethnography [4], [5] was successfully used in our research project to examine workplace literacy practices and habits of mind employed by eight engineers in their workplaces over a period of three years. The overarching purpose of the project was to develop models of disciplinary literacy instruction [6] and habits of mind [7] in engineering, both of which are potential methods for teaching students the knowledge, skills, and strategies that may prepare them for an engineering career. Disciplinary literacy instruction teaches students the ways that practitioners use literacy practices when reading, writing, interpreting, and evaluating discipline-specific information [8]. Habits of mind are the intelligent behaviors that guide how professionals respond when faced with situations of uncertainty [9]. By understanding how engineers use disciplinary literacy practices and habits of mind in the workplace, models for student instruction can be developed. These instructional practices can be used to support students’ use of authentic engineering practices and ways of thinking that will support them in the classroom and in their future workplaces. Findings about the disciplinary practices and habits of mind of the eight engineers are presented in previous publications by the authors (e.g., [10]–[12]). 

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4. An empirical analysis of high school students' practices of modelling with unstructured data

   https://doi.org/10.1111/bjet.13253  

   Jiang, Shiyan ; Nocera, Amato ; Tatar, Cansu ; Yoder, Michael Miller ; Chao, Jie ; Wiedemann, Kenia ; Finzer, William ; Rosé, Carolyn P. ( July 2022 , British Journal of Educational Technology)

   To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.

   What is already known about this topic

   Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

   While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

   There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

   What this paper adds

   Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

   Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

   Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

   Implications for practice and/or policy

   It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

   Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

   To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

    

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5. Documenting Engineering Identity: Electrical and Computer Engineering Departmental Documents and Student Identity

   https://doi.org/10.18260/1-2--30343  

   Friedensen, Rachel ; Doran, Erin ; Rodriguez, Sarah ( June 2018 , 2018 ASEE Annual Conference)

   As concerns about the preparation of engineers grow, so has interest in the dimensions of engineering identity. By having a thorough understanding of engineering identity, departments will be better able to produce engineers who understand their role as a member of the profession. Generally, engineering identity literature has not focused on specific disciplinary identities, instead looking at engineering as a whole. Previous literature has utilized role identity theory (e.g., Gee, 2001) and identified key dimensions of engineering identity, including one’s performance/competence and interest in engineering courses and recognition as a current/future engineer (Godwin, 2016; Godwin et al., 2013; Godwin et al., 2016). This paper deepens our understanding of electrical and computer engineering identities. As part of research activities associated with National Science Foundation grant looking at professional formation of socio-technically minded students, we analyzed texts and documents from an electrical and computer engineering department to examine the department’s professed priorities. Using document analysis, we answered this research question: How is a department’s commitment to undergraduate engineering identity development expressed in departmental documents? Document analysis focuses on texts to describe some aspect of the social world (Bowen, 2009). This analysis was performed with two types of departmental documents: front-facing documents (e.g., websites, newsletters) and internal documents (e.g., ABET self-studies, program evaluations) from an electrical and computing engineering department at a public research university. Analysis employed a priori and emergent coding schemas to formulate themes related to identity, performance/capability, interest, and recognition present in departmental documents (Bowen, 2009; Godwin, 2016). Specifically, we skimmed documents to ascertain inclusion status; read and coded documents in depth; and identified broader themes across documents (Bowen, 2009). One broad theme was a lack of attention to identity; another showed emphasis on technical skills/competencies. By interrogating absences, we found that there is little attention being paid to identity development or its components in these documents. In other words, these texts do not indicate that the department is invested in supporting students’ senses of interest, performance, and recognition as electrical and computer engineers. Rather, we found that these texts emphasize the acquisition of specific concepts, skills, and competencies. Overall, analysis indicated that the department does not cultivate holistic engineering student identities. The resultant implications are by no means irrelevant—a focus on identity over specific skills could increase retention, increase student satisfaction, and produce better future engineers. 

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