Ibrahim H. Yeter

This preliminary study proposes the Engineering Pedagogy Scale (EPS), a means of measuring PCK characteristics that may be important for effective engineering instruction. The EPS aims to be used for the evaluation, description, and categorization of the domains and indicators that represent the practices that an ideal teacher exhibits while teaching engineering practices in elementary classrooms. Throughout this project, existing instruments were investigated thoroughly, however, and none were suitable for the engineering context. This study exploring and developing relevant initial indicators is part of a series of studies; subsequent studies will provide specific indicators for each domain and describe field testing and analysis of the EPS. After iteratively designed discussions throughout the project, the preliminary findings indicated that the proposed observational instrument resulted in seven distinctive main domains. These domains included (1) unit-specific content knowledge, (2) engineering design process (EDP), (3) productive failure and success, (4) interdisciplinary applications, (5) questioning, (6) teamwork, and finally (7) discussion, feedback, and reflection. This study has both theoretical and practical implications. Theoretically, the study will contribute to the engineering education literature by extending the concept of PCK (Shulman, 1986) to the engineering education field and its theoretical viability in the elementary school setting. Practically, it is paramount that administrators, professional developers, curriculum specialists, and teachers come to understand what skills, pedagogies, and practices are needed to facilitate the successful implementation and improvement of engineering instruction. As such, a standard instrument that evaluates teachers’ E-PCK would help to identify where improvement is needed.

Biomimicry is an interdisciplinary design approach that provides solutions to engineering problems by taking inspiration from nature. Given the established importance of biomimicry for building a sustainable world, there is a need to develop effective curricula on this topic. In this study, a workshop was conducted twice in Singapore: once with 14 students from a local high school in Singapore, and once with 11 undergraduate students in engineering from the United States. The workshop aimed to better understand how students conceptualize biomimicry following the bottom-up and top-down biomimetic methods. The workshop contained a lecture and laboratory session, and data were collected via questionnaires, field observation, and participant presentations at the end of the laboratory session. A qualitative analysis revealed that the top-down biomimetic approach was initially understood using vague and generic terms. In contrast, the students described the bottom-up approach using precise and technical vocabulary. By naming the themes highlighting the students’ conceptualizations, it was concluded that strengthening the principle that makes the natural object unique and increasing interdisciplinary knowledge are needed to help them perform the top-down approach. The results from this work should be confirmed with a more significant number of participants, and they could help develop a curriculum to teach the two approaches effectively by providing tools to help the students generalize their ideas and abstract meaning from systems.

This paper presents the results of an investigation on pre-service teacher' conceptions of computational thinking (CT) in Singapore prior to a two-hour introductory module on CT. Of 407 teachers, 280 provided valid responses to the pre-survey, which included questions on teachers' school subjects, current understandings of CT, confidence in their understandings of CT, and sources of the understandings. We deductively coded the open-ended responses through thematic analysis using four categories from a synthesis review on teachers' preconceptions of CT. The participants were classified into three groups, including STEM (primarily sciences and mathematics), non-STEM (e.g., humanities and languages), and mixed-disciplines (e.g., science and English language arts). The findings of the pre-survey showed that 42% of respondents (n=118) reported no prior knowledge of CT. Among the remaining 162 responses, the most popular view of CT was problem solving using various kinds of thinking, such as "logic", "abstraction", "step-by-step", and "decomposition" (n=106). STEM and mixed disciplines teachers (33%) reported higher levels of confidence compared to non-STEM teachers (15%). A higher percentage of STEM (64%) and mixed-disciplines (60%) pre-service teachers indicated learning about CT from formal courses during their university studies or teacher training, compared to non-STEM teachers (52%). This suggests that schools of education can play a bigger role in expanding CT awareness among pre-service teachers from non-STEM backgrounds. Finally, implications for teacher education are widely discussed.

The purpose of this study was to investigate how the “Making a Makerspace” (MM) program can enhance children’s maker literacy and teachers’ understanding of maker education in makerspaces. The program was carried out in two kindergartens in China for three months and involved 407 children and 24 teachers. Six classrooms in each kindergarten were either assigned to the Makerspace condition or the control condition. Results from teacher questionnaires and observations of the children’s maker activities showed significant improvements in problem finding & solving, hands-on, creative design, and communication skills among the children in the Makerspace condition compared to the control condition. The study revealed that children in 4- and 5-year-old classes were proficient in using the Engineering Design Process in maker activities, but this was not the case for 3-year-old classes. The teachers also reported positive experiences with the MM program. This study highlights the effectiveness of makerspaces in early childhood education and provides a practical case for implementing makerspaces in kindergarten classrooms.

Recent years have seen a high volume of computational thinking (CT) review studies. However, there have been no existing studies that map these reviews with the goal of achieving comprehensive understanding of the field of CT. This paper utilizes Tikva & Tambouris’ (2021) K-12 CT research domain conceptual model as the basis for identifying and defining CT reviews, then maps the identified 38 CT reviews onto the identified domains. We pinpoint eight potential future review topics, including "communities" of tools, "modeling simulations," "problem-solving" and "scaffolding" of learning strategies, "demographic attributes" of factors, "practices" and "perspectives" of the knowledge-based areas, and the "teacher training" of capacity building. We also examine the topical keywords of the reviews and identify that the scope of the term "unplugged" is vaguely defined among the existing research, suggesting a need to refine the definition of this frequently discussed topic so as to be able to more effectively conduct supplementary reviews. Our results help to better understand the CT review field and formulate future directions.

This work-in-progress reports on a project to establish differences between STEM education, and specifically Engineering Education, and other education fields/disciplines based on empirical observations. In this study we report on first steps towards a literature overview of such differences and on the development of an analytical framework to analyze the publications.

Background Data literacy is increasingly important in today’s data-driven world. Students across many educational systems first formally learn about data in elementary school not as a separate subject but via the mathematics curriculum. This experience can create tensions in the priorities of learning and assessment given the presence of other foundational mathematics content domains such as numbers, algebra, measurement, and geometry. There is a need to study data literacy in comparison to these other content domains in elementary mathematics. To address this need, we developed a methodology motivated by thinking curriculum theory and aligned with international assessment framework, for comparative analysis across mathematics content domains. This methodology examined increasing levels of cognitive domains from knowing to applying to reasoning across mathematics content domains. Intended, assessed, and attained curricula were analyzed using Singapore as a case study, combined with broader comparisons to attainments in four East Asian countries in TIMSS, an international large-scale assessment. Results We found that learning in the data domain had very limited coverage in intended and assessed curricula in Singapore. However, compared to other mathematics content domains, the data curriculum placed heavier emphasis on higher-order cognitive domains including the use of generally difficult mixed data visualizations. This demanding curriculum in Singapore was associated with the highest attainment in the data domain among average 4th grade Singaporean students relative to students in four East Asian countries in TIMSS, as analyzed by quantile regression. However, lower-performing Singaporean students at the 10th percentile generally did not outperform their East Asian peers. We further found very limited applications of data in other mathematics domains or cross-domain learning more generally. Conclusion Our study offers a comparative analysis of the data curriculum in elementary school mathematics education. While the data curriculum was cognitively demanding and translated to very high average attainments of Singaporean students, the curriculum did not equally help weaker Singaporean students, with implications on current discourse on equity–excellence trade-off in science, technology, engineering, and mathematics (STEM) education. Our study further highlights the lack of cross-domain learning in mathematics involving data. Despite the broad applicability of data science, elementary school students’ first formal experience with data may lack emphasis on its cross-domain applications, suggesting a need to further integrate data skills and competencies into the mathematics curriculum and beyond.

This research is to practice a full paper that discusses ethics in engineering. Engineering graduates are expected to have ethical critical thinking and problem-solving skills to tackle real-world complex problems in the workplace. Course curriculum could benefit from more authentic learning and interdisciplinary teaching focused on engineering design and problem-solving. This pilot project incorporates a research-based design thinking framework EDIPT (Empathize, Define, Ideate, Prototype, Test), developed by Stanford University to guide students through conceptualization-to-production processes in a newly designed engineering course at an internationally-renowned university in Singapore. The study aims to equip students for ethical problem-solving, support more innovative and feasible ideas and products, and allow students to better exhibit knowledge and accomplish the Engineering Accreditation Board (EAB) requirements. 36 third-year engineering students (39% female and 61% male) participated in this study through hybrid online/offline course activities and working with industry partners for real-world problem-solving. While the entire project implements an exploratory sequential mixed method research design, with multi-layered research data including student interviews and in-course and post-course reflections, this paper focuses on the participants’ open-ended pre-course survey responses about ethics in engineering. We conducted qualitative inductive analysis using an open coding technique and created descriptive codes. Preliminary findings suggest five emergent themes of ethical considerations, namely 1) client-centered responsibility, 2) intellectual property infringement/originality, 3) macro ethical considerations, 4) professionalism, and 5) others. Findings from this study will help to bolster research on ethical considerations in design thinking for the engineering field, as well as the applicability of foreign research frameworks in local practice contexts. Findings will also contribute to determining the best approach for improving the teaching framework for future iterations of the engineering courses, as well as assessing the suitability of applying design thinking to similar capstone courses within the university.

Although Machine Learning (ML) is used already in our daily lives, few are familiar with the technology. This poses new challenges for students to understand ML, its potential, and limitations as well as to empower them to become creators of intelligent solutions. To effectively guide the learning of ML, this article proposes a scoring rubric for the performance-based assessment of the learning of concepts and practices regarding image classification with artificial neural networks in K-12. The assessment is based on the examination of student-created artifacts as a part of open-ended applications on the use stage of the Use-Modify-Create cycle. An initial evaluation of the scoring rubric through an expert panel demonstrates its internal consistency as well as its correctness and relevance. Providing a first step for the assessment of concepts on image recognition, the results may support the progress of learning ML by providing feedback to students and teachers.

The past two decades have shown a rising global trend to offer online K-12 STEM learning, necessitating teachers to have the knowledge and skills to navigate online teaching contexts. However, related professional development and online STEM best teaching practices remain to be fully articulated. This issue was exacerbated following the COVID-19 pandemic outbreak that pushed most teachers into emergency remote teaching (ERT) roles without preparation. As such, this study explores secondary master STEM teachers’ (e.g., > 8 years of STEM teaching experience) transitions to ERT, classifying and categorizing what tensions they encountered in the process. Survey methods and open coding were used to collect and analyze data emphasizing teachers’ perceived challenges in shifting to ERT. Findings suggest that while participants had considerable STEM teaching experience, they encountered converging conceptual, pedagogical, cultural, and political tensions connected within ERT contexts. Results offer tangible starting points for supporting teachers in transitioning to online STEM environments.