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The chapter presents the implementation of ethics education via challenge-based learning (CBL) in three European settings. At TU Eindhoven (the Netherlands), a mandatory first-year User, Society, and Enterprise course on the ethics and history of technology offers a CBL alternative on ethics and data analytics in collaboration with internal student and research teams. The University of Lübeck (Germany) initiated the project CREATE – Challenge-based Learning for Robotics Students by Engaging Start-Ups in Technology Ethics, which enables 60 students in Robotics and Autonomous Systems to integrate ethical and societal considerations into technological development processes, in cooperation with start-ups from a local accelerator. In Spain, CBI-Fusion Point brings together 40 students from business and law (ESADE), engineering and technology (Polytechnic University of Catalonia), and design (IED Barcelona Design University) for an innovation course focused on the application of CERN-developed technologies to real-world problems. The chapter documents the process of setting up three CBL courses that engage students with grand societal topics which require the integration of ethical concerns from the design stage of technological development. The authors also reflect on the challenges of teaching ethics via CBL and the lessons they learned by delivering experiential learning activities rooted in real-life challenges and contexts marked by high epistemic uncertainty. The contribution reflects the transition to remote teaching and presents strategies employed to enhance online communication and collaboration. The chapter thus provides guidance for instructors interested in teaching ethics via CBL and recommends further lines for action and research.

The White House Initiative: Educate to Innovate (2009) outlines the need for school age children (P-12) to focus more intentionally on Science, Technology, Engineering, and Math or STEM. The arts and other developmentally appropriate activities (i.e., blocks, painting, music, etc.) are added to STEM to create STEAM. Specifically, this chapter focuses on Technology, Engineering, and the Arts within the contexts of Science and Mathematics in the early childhood setting. By allowing children the time to explore and create, young children will wonder about the world around them. The chapter concludes with suggestions for early childhood professionals to create environments (physically, temporally, and interpersonally) that encourage and expand the STEM principles.

This chapter reviews student engagement and learning over of a six year study period (>500 students) in a technology rich learning environment. The technology rich learning environment in this project consists of tablet PCs for each student (1:1 environment), visually immersive multiple projection screens, and collaborative digital inking software. This chapter reviews the education problem being addressed, and the learning theory used as a lens to focus specific active learning pedagogical techniques to address the educational problem. From this problem-based learning theory grounded approach, the features desired in a technology rich learning environment were developed. The approach is shared in this chapter with specific detailed examples to allow others to implement technology rich learning environments with active learning pedagogical approaches to address specific education problems in their institution. The technology rich learning environment implemented and studied includes multiple hardware/software pieces to create a system level solution versus a single device or single app solution.

Challenge-based learning (CBL) is a trending educational concept in engineering education. The literature suggests that there is a growing variety in CBL implementations, stemming from the flexible and abstract definition of CBL that is shaped by teachers' perceptions. The chapter discusses how the CBL concept has been developed at Eindhoven University of Technology and describes the development and use of two educational resources aimed to facilitate conceptualization, design, and research of CBL for curriculum designers and teachers. The first resource is a set of CBL design principles for framing the variety of CBL and providing teachers with advice about how to develop CBL courses within an overall CBL curriculum. The second resource is a curriculum-mapping instrument called the CBL compass, which aims at mapping CBL initiatives and identifying gaps, overlaps, and misalignments in CBL implementation at a curriculum level. Both CBL design principles and the CBL compass have been developed by combining insights from theory and practical examples of CBL at TU/e into a higher order model of vision, teaching and learning, and support. We discuss the two educational instruments and showcase their application in the Eindhoven Engineering Education (E³) program, and we discuss preliminary findings and insights. The chapter concludes with recommendations for future practice and research.

Countless pundits have referred to young African American males as an “endangered species.” While this description of the state of African American male youth between the ages of 18 and 25 years can be said to apply in many different circumstances, nowhere is it more apt than in engineering education. Their rates of matriculation, persistence, and graduation in engineering trail not only those of their white, Latino, and Asian counterparts but those of African American females as well.

A wave of technological change in the first decades of the twenty-first century is prefiguring a fundamental restructuring of society. Key among the driving forces behind such change are powerful technologies with the potential to exert major transformations on a range of human activities and, crucially, to do so without direct human intervention. The technologies collectively referred to as Artificial Intelligence, or AI represent a productive lens through which to investigate two interrelated transformations: the emergence of self-driving cars and the coming shifts in education. This is in particular because AI’s versatility has led it to be directly applied (and increasingly valued) both in new automated driving technologies, and in the development of new forms of instruction. From the educational perspective, this means that the same technologies that are transforming workforce conditions are also reshaping – directly and indirectly – the approaches, objectives, and experiences of students and educational institutions. This chapter lays out how these twin transformations are likely to play out in the case of the automotive industry and the educational pathways of two occupations closely associated with it: automotive engineers and repair technicians. Two key arguments underpin this examination. First, educational programs for these two occupations, (and beyond) should be broadened to develop versatility and adaptability through tools and perspectives that allow people to move vertically within organizations and laterally across industries in the face of rapid technological change. Second, these educational programs must explicitly tackle AI and the coming technological revolution from a variety of dimensions that connect technical skills acquisition with the context on how these technologies are incorporated in society, how they are governed, and what are the various responses to them. This will allow students and professionals to navigate a rapidly changing labor landscape better while endowing them with the vocabulary to actively participate in the debates that shape its construction.

In this paper, we examine the occupational distribution of individuals who hold bachelor degrees in particular fields in the United States using data from the various waves of the National Survey of College Graduates. We propose and calculate indices that describe two related aspects of the occupational distribution by major field of study: distinctiveness (how dissimilar are the occupations of a particular major when compared with all other majors) and variety (how varied are the occupations among those who hold that particular major). We discuss theoretical properties of these indices and statistical properties of their estimates. We show that the occupational variety has increased since 1993 for most major fields of study, particularly between the 1993 and 2003 waves of the survey. We explore reasons for this broadening of the occupation distribution. We find that this has not led to an increase in reported mismatch between degree and occupation.