Journal of Pre-College Engineering Education Research (J-PEER) Annual Report from January 1, 2021, to December 31, 2021

This annual report includes the Journal of Pre-College Engineering Education Research’s readership metrics and statistics, authorship metrics and trends, and our reflections on 2021. In the last year, the Journal of Pre-College Engineering Education Research started to publish special issues and the editorial team has been working to transform pre-college engineering education

Validating the Critical Engineering Literacy Test (CELT) with Cognitive Interviews

The CELT learning assessment focuses on the critical thinking skills of engineering students and allows for the improvement of information literacy, one’s ability identifying and implementing important information. With the pairing of cognitive interviewing, the CELT will allow for the identification of errors and will better determine the reasoning in the decisions made by the students taking the test. The CELT test uses multiple choice and open response questions to evaluate the students and the areas where improvements need to be made. Over the course of the year, the students’ information literacy skills will be expanded and then re-evaluated at the end of the year. Students who are taking the test will be paired with evaluators to explain their thought process and the steps they take to reason through their answers after they complete each of the questions. By applying the cognitive interview with the examination, the CELT test will allow the research team to identify areas of improvement for the CELT learning assessment. These areas can be identified and addressed to more properly evaluate the students and provide a test to better evaluate the knowledge and growth of the engineering students. Cognitive interviewing as part of the assessment will help add more depth and information to the research process to allow for more critical thinking application and assessment to be provided by the student

The Inaugural Readership and Authorship Report of the Journal of Pre-College Engineering Education Research (J-PEER)

The Journal of Pre-College Engineering Education Research (J-PEER) approaches a decade of publications since its launch in 2011. This inaugural report presents metrics and statistics on J-PEER\u27s readership and authorship, looking specifically at data from 2019, along with reflections from the founding and immediate-past editors

Journal of Pre-College Engineering Education Research (J-PEER) Annual Report from January 1, 2022, to December 31, 2022

In this annual report, we present our reflections on 2022 along with the Journal of Pre-College Engineering Education Research (JPEER) readership trends and authorship metrics. In 2022, J-PEER published two issues in volume 12 comprised of 17 articles. The second issue of the year included a special issue on the impact of COVID-19 on education, marking the impact that the pandemic had on pre-college engineering education

Journal of Pre-College Engineering Education Research (J-PEER) Annual Report from January 1, 2020 to December 31, 2020

Over the last ten years, the Journal of Pre-College Engineering Education Research (J-PEER) has been disseminating research that seeks to investigate, enhance, and transform pre-college engineering education and, ultimately, to create an engineering‐literate society. The 2020 annual report presents readership metrics and statistics of the decade, trends and metrics on J-PEER\u27s authorship in 2020, and our reflections on the last year

Understanding Informed Design through Trade-Off Decisions With an Empirically-Based Protocol for Students and Design Educators

Trade-off decisions, which necessitate striking a balance between two or more desirable but competing features, are a crucial part of design practice. However, they are known to be difficult for student designers to make. While designers, educators, and researchers have numerous methods to assess the quality of design artifacts, these methods are not necessarily easy to use, nor do they indicate design competency. Moreover, they are not grounded in a definition of engineering design. The objectives of this study were twofold. First, we developed a protocol to depict design artifact quality through the lens of design trade-off decisions. We aimed to produce a protocol that:(1) encompasses multiple complementary and competing dimensions, (2) can be applied consistently and systematically, and (3) indicates design competency. We conceptualized a quantitative representation of the degree to which a design artifact addresses human, technical, and economic requirements called the Trade-off Value Protocol. Second, we tested the Trade-off Value Protocol by applying it to 398 middle school students’ design artifacts of energy-efficient homes. We used an etic approach of thematic analysis to identify the patterns of variation therein. We found five distinct patterns of variation in the set of student design artifacts, which suggested certain trends in the way that students address design dimensions and demonstrate varying levels of design competency. The Trade-off Value Protocol isolates an important feature of design competency with which beginning designers often struggle and could be a tool for educators to help students become more informed designers

Engineering Design Coaching Tool

The Engineering Design Coaching Tool is an instructional tool designed to elicit student reasoning and guide design decisions. We present the Engineering Design Coaching Tool in a fillable version and in the context of the Solarize Your World curriculum. We recommend using the coaching tool for stimulating learning during formal and informal design review sessions

A Cross-Case Analysis of Disciplinary Identities Communicated Through Design Reviews

In post-secondary educational settings, discourse is a mechanism by which students develop occupational identities as they engage in a particular community that communicates attributes of their prospective profession. This study focuses on revealing disciplinary identities and how they are conveyed and negotiated during interactions between design students and project reviewers. We draw upon Gee’s identity framework and focus on the enactments of disciplinary identity in three different disciplinary settings: choreography, industrial design, and mechanical engineering. A cross-case analysis indicated differences that were epistemological (e.g., subjectivity of reviews) and similarities in ways instructors modeled institutional identities. The results have implications for interdisciplinary activities and suggest that disciplines that engage in design have much to learn from one another

Large-scale Research on Engineering Design in Secondary Classrooms: Big Learner Data Using Energy3D Computer-Aided Design

Large-scale Research on Engineering Design in Secondary Classrooms: Big Learner Data Using Energy3D Computer-Aided Design Through a five-year collaborative project, the Concord Consortium and PurdueUniversity are applying a data-intensive approach to study one of the most fundamental researchtopics in learning sciences: “How do secondary students learn and apply science concepts inengineering design processes?” We have collected more 2GB of structured data from secondaryschool students in Indiana and Massachusetts through automatic, unobtrusive logging of studentdesign processes enabled by a unique CAD tool that supports the design of energy-efficientbuildings using Earth science and physical science concepts. Data includes fine-grainedinformation of student actions, experimentation results, electronic notes, and design artifacts.These process data are used to reconstruct the entire learning trajectory of each individualstudent with high resolution. Our research evaluates how these learning analytics applied to theseprocess data can be the computational counterparts of traditional performance assessmentmethods. Combining these process data with pre/post-tests and demographic data, we haveinvestigated the common patterns of student design behaviors and how they are associated withlearning outcomes with a specific focus on how students deepen their understanding of scienceconcepts involved in engineering design projects and how often and deeply students usescientific experimentation to make a design choice. So far we completed two small-scale studiesin Massachusetts and one study in Indiana using classroom observations and expert evaluations.We are collecting data with student interviews to validate metrics. Some key findings are…evidence that suggests that for science learning to occur, design projects used in classroomsshould (1) allow and emphasize trade-off analysis and include time and resources forexperimenting and data gathering; (2) provide instructional scaffolding and formative feedbackto guide student design

Identifying Key Factors of Engineering Innovativeness

Identifying key factors of engineering innovativenessSignificant resources are spent nationally and locally to foster innovation, yet limited research exists onthe personal characteristics of innovators, especially those found in engineering. This three-yearcollaborative research project, currently in its second year, has led to the identification of specificattributes associated with engineering innovators, with the potential to inform a broad range of people,from engineering students to engineering educators to practicing engineers and their managers. Throughthis project, we have developed a socially constructed set of key engineering innovativenesscharacteristics based on the views of a diverse group of engineering innovation experts. We have alsodetermined which characteristics are more important in specific stages of the innovation process, andexamined the innovativeness factors that are innate/fixed (e.g., cognitive style) versus those that can bechanged/enhanced (e.g., knowledge, skills).Through a series of interviews and a Delphi study with engineering innovators from academia andindustry, we found the innovation process to have at least three distinct stages: (1) the front-end ordiscovery stage, (2) the middle or development stage, and (3) the back-end or implementation andadoption stage. For example, some of the key characteristics of engineering innovativeness that wereidentified with the discovery stage of the innovation process included: experimenter, alternatives seeker,curious, risk taker, and visionary. Future research steps will focus on testing and widely disseminating avalidated instrument that assesses the key engineering innovativeness characteristics, and using ourresearch results to inform the education of innovative engineers and the professional development ofengineering practitioners