Yeter, Ibrahim H.

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.

Artificial intelligence (AI) has permeated most facets of life in the 21st century and has rapidly transformed various aspects of modern society. From entertainment to education, these advanced technologies have achieved a high level of competency in skills that once necessitated human involvement. Given AI's potential impact, ensuring students are literate in AI will support the careful integration of these advanced technologies to achieve sustainable development goals. This review hence examines the avenues for integrating AI literacy into elementary education by analyzing current global initiatives focused on implementing AI literacy education. The purpose is to support innovations within the educational framework to develop a universally accessible AI literacy education program. In line with this purpose, this study explores worldwide AI literacy initiatives that use hands-on activities, collaborative learning, and project-based learning to introduce AI fundamentals to diverse learners. Limitations on the provision of AI literacy education are also discussed, including professional development, openness to AI tools, and other challenges. This review aims to inform global efforts to support universal access to AI literacy education, which can ensure equitable outcomes for all learners, emphasizing the need for collaborative efforts to support the development and delivery of quality AI literacy education.

Makerspaces are used to promote classroom change and creativity for the 21st century. Building on the learning theory of Constructionism, this intervention study used a curriculum intervention program, “Making a Makerspace” (MM), to integrate the makerspace into Chinese kindergartens. We used a quasi-experimental research design to evaluate the effects of this curriculum intervention, with 120 children enrolled in the experimental classrooms, while the other 111 children enrolled in the waitlist control classrooms. Teacher-report child performance (N = 231) showed that the MM program resulted in significantly higher scores in children's STEM habits of mind in the intervention group, relative to the control group. Analyses of parent-report child behaviors revealed that there was a significant effect of the MM program on post-intervention temperamental surgency. Our evidence shows that such a scalable program encourages and guides teachers to build a positive learning environment for supporting young children's making and thinking in everyday preschool experiences. The makerspace further sets a solid foundation for the development of children's STEM thinking skills and socioemotional skills in a rapidly changing digital society.

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.

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.

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 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.

The notion of teaching experts’ habits of mind (e.g., computational thinking and scientific thinking) to novices seems to have inspired many educators and researchers worldwide. In particular, a great deal of efforts has been invested in computational thinking (CT) and its manifestations in different fields. However, there remain some troubling spots in CT education as far as how to teach it at different levels of education. The same argument applies to teaching scientific thinking (ST) skills. A remedy has been suggested to narrow CT and ST skillsets down to core cognitive competencies so they can be introduced in early and middle grades and continue to be nurtured during secondary and post-secondary years. Neuroscientists suggest that the act of (computational) thinking is strongly linked to the acts of information storage/retrieval by our brain. Plus, years of research have shown that retrieval practices promote not only knowledge retention but also inductive reasoning and deductive reasoning. Not surprisingly, these reasoning skills are core elements of both CT and ST skillsets. This article will mesh the findings of a teacher professional development with the existing literature to lay a claim that retrieval practices enhance CT and ST skills. The study offered training to secondary school teachers (n = 275) who conducted classroom action research to measure the impact of retrieval practices on teaching and learning of STEM and CT concepts. We used a quasi-experimental research design with purposeful sampling and a sequential mixed-methods approach focusing on the impact of professional development on teacher outcomes and, in turn, on student outcomes. A survey of teacher participants showed that the majority (96%) of survey respondents (n = 232) reported a good understanding of retrieval strategies, and how relevant ideas can be implemented and tested in the classroom. A large number of action research (target-control) studies by teachers (n = 122) showed that students who learned STEM and CS concepts through retrieval practices consistently scored 5–30% higher than those using the usual blocked practice. In most cases, the difference was statistically significant (p < 0.05). While the study contributes to retrieval practices literature, those looking for best practices to teach core CT and ST skills should benefit from it the most. The study concludes with some recommendations for future research based on the limitations of its current findings.

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.