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1. I Graduated, Now What? An Overview of the Academic Engineering Education Research Job Field and Search Process

   https://doi.org/10.18260/1-2--34741  

   McCave, Erin ; Bodnar, Cheryl ; Smith-Orr, Courtney ; Strong, Alexandra ; Lee, Walter ; Faber, Courtney ( January 2021 , 2020 ASEE Virtual Annual Conference)

   null (Ed.)

   As the field continues to grow, engineering education is continually challenged with finding engineering education research (EER) positions that align with the broad abilities and interests of its members. EER positions exist in engineering education departments, traditional engineering departments (e.g., mechanical, civil), and in non-degree granting programs (e.g., centers for teaching and learning, engineering programs). These positions vary across their emphasis on research, teaching, and service and provide access to different resources and mechanisms to impact engineering education. Given the range of positions available in EER and the emergence of new EER programs, it can be challenging for graduate students and postdocs to navigate the job search process and identify a position that aligns with their professional goals. The purpose of this research was to better understand the EER job market as it relates to what applicants (i.e., graduates and post-docs) experience as they navigate the job-search and decision-making process. For this study, we conducted interviews with seven transitioning first-year EER faculty members. These individuals were transitioning into various EER faculty positions (e.g. Lecturer, Teaching Fellow, Assistant Professor, Research Assistant Professor) with different backgrounds in EER based on their graduate training experiences which included established EER programs as well as traditional engineering departments with EER advisor(s). We asked questions that focused on the individual’s new faculty position, their perception of the weekly time requirements, their job search process, and factors that influenced their final decision of which job to select. Each interview was conducted by two graduate students and was then transcribed and verified for accuracy. Three faculty members performed holistic coding of the transcripts focused on three areas: EER position types, job search process, and job decision making process. The Qualifying Qualitative research Quality framework (Q3) was used as a guide throughout our data collection and analysis process to ensure reliability and trustworthiness of the data collected. Through our analysis process, we developed a visual representation that provides a guide to assist EER graduate students and postdocs with their job search process. The first figure captures the diversity of positions along with the types of institutions where these positions exist to provide a starting point for individuals on their job search process. The second figure includes a timeline to help capture the average time frames for different phases of the job search process. Factors associated with final decisions based on the interviews conducted are also outlined to provide areas of consideration for individuals undergoing this process in the future. This work provides insight to aspiring academics about the range of opportunities available to those with a background in EER and how they can pursue finding alignment between their interests and positions that are available. 

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2. The Impact of Pre-Service Teachers’ Perceptions of Engineering on Their Self-Efficacy with Teaching Engineering

   Chesnutt, Betsy ; Mountain, Daniel ; Faber, Courtney ( July 2023 , ASEE annual conference exposition)

   Although engineering is becoming increasingly important in K-12 education, previous research has demonstrated that, similar to the general population, K-12 teachers typically hold inaccurate perceptions of engineering, which affects their ability to provide students with relevant engineering experiences. Studies have shown that K-12 teachers often confuse the work of engineers with that of automotive mechanics or construction workers or assume that engineering is only for “super smart” students who are naturally gifted or who come from higher socioeconomic backgrounds. This indicates that many teachers do not understand the nature of engineering work and have stereotypical attitudes about who is qualified to be an engineer. These inaccurate perceptions of engineering among K-12 teachers may influence the way that teachers introduce engineering practices to their students and make connections between engineering and the other STEM disciplines. In addition, teacher self-efficacy has been shown to not only influence teachers’ willingness to engage with a particular topic, but also to have a significant influence on the motivation and achievement of their students. Research also indicates that high-efficacy teachers typically exert more effort and utilize more effective instructional strategies than low-efficacy teachers. The goal of this study was to examine the perceptions that pre-service K-12 teachers hold about engineers and engineering, and to further explore how those perceptions influence their self-efficacy with teaching engineering and beliefs about what skills and resources are necessary to teach engineering in a K-12 classroom. We first developed a survey instrument that included questions taken from two previously published instruments: the Design, Engineering, and Technology survey and the Teaching Engineering Self-Efficacy Scale for K-12 Teachers. Forty-two students enrolled in an undergraduate program at {Name Redacted} in which students simultaneously pursue a bachelor’s degree in a STEM field and K-12 teacher licensure completed the survey. Based on survey responses, six participants, representing a range of self-efficacy scores and majors, were selected to participate in interviews. In these interviews, participants were asked questions about their perceptions of engineers and were also asked to sort a list of characteristics based on whether they applied to engineers or not. Finally, interview participants were asked questions about their confidence in their ability to teach engineering and about what skills and/or resources they would require to be able to teach engineering in their future classrooms. The results of this study indicated that the participants’ perceptions of engineering and engineers did impact their self-efficacy with teaching engineering and their beliefs about how well engineering could be incorporated into other STEM subjects. A recurring theme among participants with low self-efficacy was a lack of exposure to engineering and inaccurate perceptions of the nature of engineering work. These pre-service teachers felt that they would not be able to teach engineering to K-12 students because they did not personally have much exposure to engineering or knowledge about engineering work. In future work, we will investigate how providing pre-service teachers with training in engineering education and exposure to engineers and engineering students impacts both their perceptions of engineering and self-efficacy with teaching engineering. 

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3. Preparing Pre-Service Teacher Candidates for the Praxis Exam: An Innovative Model of Blended Support

   https://doi.org/10.1145/3328778.3372654  

   Odom-Bartel, Rebecca ; Fletcher, Carol ; Owen, John ; Gray, Jeff ; Zelkowski, Jeremy ( February 2020 , ACM Technical Symposium on Computer Science Education (SIGSCE))

   null (Ed.)

   The expansion of K-12 computer science (CS) has driven a dramatic need for educators who are trained in CS content and pedagogy [1]. This poster describes our effort to train teacher candidates (i.e., pre-service teachers who are students seeking degrees within a College of Education), who are specializing in secondary mathematics education, to be future CS educators. We specifically describe our collaboration to provide a blended preparatory six-week training for the ETS CS Praxis exam (5652), assisting our pre-service students in satisfying the CS certification requirements in our state before they graduate and begin their professional teaching career. Given the unique challenges of pre-service CS teacher preparation [2], blended models, which combine both in-person and online instruction, are an effective approach to building a pre-service program. Within our pre-service CS program, students first complete a two-course pathway that prepares them in AP CSP content and pedagogy experiences, including observations in local AP CSP classrooms [3]. After completing the two courses, our students participate in the blended version of the WeTeach_CS Praxis preparation course to achieve certification. The in-person support provided by the blended model contributed significantly to certification success in this project. With a cut-score of 149 for the Praxis exam, all 11 of our pre-service students who completed the course received a passing score (including one student with a perfect score of 200, and another student with a 195); the average score for our pre-service students was 175. An additional 11 in-service teachers, with diverse backgrounds in CS content knowledge, also participated in the blended Praxis preparation course, with an average score of 166. Given the unique challenges of pre-service CS teacher preparation, university pre-service CS teacher programs should look to innovative models of teacher support developed by in-service programs to make substantial gains in CS teacher certification. Incorporating an asynchronous online course that allows teachers with a wide range of prior experience in CS to learn at their own pace with in-person coursework and support appears to be a viable model for assisting non-CS major teacher candidates in achieving a CS certification. With the blended model, even teachers with no background knowledge in CS were successful. Within our pre-service CS program, students first complete a two-course pathway that prepares them in AP CSP content and pedagogy experiences, including observations in local AP CSP classrooms [3]. After completing the two courses, our students participate in the blended version of the WeTeach_CS Praxis preparation course to achieve certification. The in-person support provided by the blended model contributed significantly to certification success in this project. With a cut-score of 149 for the Praxis exam, all 11 of our pre-service students who completed the course received a passing score (including one student with a perfect score of 200, and another student with a 195); the average score for our pre-service students was 175. An additional 11 in-service teachers, with diverse backgrounds in CS content knowledge, also participated in the blended Praxis preparation course, with an average score of 166. Incorporating an asynchronous online course that allows teachers with a wide range of prior experience in CS to learn at their own pace with in-person coursework and support appears to be a viable model for assisting non-CS major teacher candidates in achieving a CS certification. With the blended model, even teachers with no background knowledge in CS were successful. 

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4. Effects of High Impact Educational Practices on engineering and computer science student participation, persistence, and success at land grant universities: YEAR 2.

   Asghar, M. ( August 2022 , 2022 ASEE Annual Conference & Exposition)

   Effects of High Impact Educational Practices on Engineering and Computer Science Student Participation, Persistence, and Success at Land Grant Universities: Award# RIEF-1927218 – Year 2 Abstract Funded by the National Science Foundation (NSF), this project aims to investigate and identify associations (if any) that exist between student participation in High Impact Educational Practices (HIP) and their educational outcomes in undergraduate engineering and computer science (E/CS) programs. To understand the effects of HIP participation among E/CS students from groups historically underrepresented and underserved in E/CS, this study takes place within the rural, public university context at two western land grant institutions (one of which is an Hispanic-serving institution). Conceptualizing diversity broadly, this study considers gender, race and ethnicity, and first-generation, transfer, and nontraditional student status to be facets of identity that contribute to the diversity of academic programs and the technical workforce. This sequential, explanatory, mixed-methods study is guided by the following research questions: 1. To what extent do E/CS students participate in HIP? 2. What relationships (if any) exist between E/CS student participation in HIP and their educational outcomes (i.e., persistence in major, academic performance, and graduation)? 3. How do contextual factors (e.g., institutional, programmatic, personal, social, financial, etc.) affect E/CS student awareness of, interest in, and participation in HIP? During Project Year 1, a survey driven quantitative study was conducted. A survey informed by results of the National Survey of Student Engagement (NSSE) from each institution was developed and deployed. Survey respondents (N = 531) were students enrolled in undergraduate E/CS programs at either institution. Frequency distribution analyses were conducted to assess the respondents’ level of participation in extracurricular HIPs (i.e., global learning and study aboard, internships, learning communities, service and community-based learning, and undergraduate research) that have been shown in the literature to positively impact undergraduate student success. Further statistical analysis was conducted to understand the effects of HIP participation, coursework enjoyability, and confidence at completing a degree on the academic success of underrepresented and nontraditional E/CS students. Exploratory factor analysis was used to derive an "academic success" variable from five items that sought to measure how students persevere to attain academic goals. Results showed that a linear relationship in the target population exists and that the resultant multiple regression model is a good fit for the data. During the Project Year 2, survey results were used to develop focus group interview protocols and guide the purposive selection of focus group participants. Focus group interviews were conducted with a total of 27 undergraduates (12 males, 15 females, 16 engineering students, 11 computer science students) across both institutions via video conferencing (i.e., ZOOM) during the spring and fall 2021 semesters. Currently, verified focus group transcripts are being systematically analyzed and coded by a team of four trained coders to identify themes and answer the research questions. This paper will provide an overview of the preliminary themes so far identified. Future project activities during Project Year 3 will focus on refining themes identified during the focus group transcript analysis. Survey and focus group data will then be combined to develop deeper understandings of why and how E/CS students participate in the HIP at their university, taking into account the institutional and programmatic contexts at each institution. Ultimately, the project will develop and disseminate recommendations for improving diverse E/CS student awareness of, interest in, and participation in HIP, at similar land grant institutions nationally. 

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5. 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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