oftenrequiring interdisciplinary teamwork. Students need to negotiate a range of viewpoints, including avariety of specialties, and balance their unique contributions to form a coherent whole. Teamworkis a necessary skill for engineers with its significance recognized by ABET (Accreditation Boardfor Engineering and Technology): Criterion 3, Student Outcome 5 - “Students should be able tofunction effectively as members of a technical team, and as leaders on technical teams”.Teamwork is often the key to solving the complex problems engineers face. One goal of higher education is to prepare students for their professional lives. Teamworkis imperative to solve “real-world” problems [3]. Teamwork is a highly important skill forengineers to have
thatemulated the CPU Usage History graph on a computer. The objective of this representation was“to provide an accurate representation of the data in real-time.” These timelines were createdusing a custom Java program that manipulated the data so that design activities receiving theprimary focus at any given time were drawn at the top of the graph while other, shorter durationand overlapping activities appeared as peaks reaching up from the bottom. For example, note theteal/green colored line for Modeling that appears across most of the top of the graph in Figure 11(since Modeling was Senior B’s dominant activity), while other activities spiked up below it,occasionally pulling the Modeling line down as they competed for precedence. Tim wrote that
learning. This cycle is also the foundation on which learning statements are based,which we explore in Section 3. Additionally, Balmer builds on the work of Mistree et al. [3]whose work on project-based design education includes open design projects that are composedof a technical problem and problem context, which together are used to create an authentic, real-world experience. In agreement with the work of Dym et al. [2], we assert that preparingengineers for existing challenges faced by professional working engineers necessarily requiresexposure to open problems that force students to confront unknown variables, non-prioritizedrequirements, and situational context. Furthermore, we leverage the work of ABET [5], Eggert[6], Lahidji [7], and
guidance for understanding and improving the design ofmakerspaces and similar learning environments.1. INTRODUCTIONEach year students arrive at the steps of engineering colleges eager to become engineers.University makerspaces have emerged as a space where engineering students appear increasinglydrawn. An academic makerspace is part workshop, part classroom, and part community ofpractice. It is a place where real-world challenges are married with hands-on approaches; wherestudents are encouraged to prototype and realize ideas; where design meets manufacturing; andwhere a student’s mind, hands, and heart can be integrally intertwined. Learning in makerspacesis different than learning in typical engineering labs or classrooms not merely because of
]. Avariety of researchers have found that small group learning environments benefit girls [28], [48],[52] – [57]. Hands-on activities that emphasize applications of knowledge in real-world contextscan meet girls’ desire to know how their learning can be applied [55], [58], [59].Despite a number of beneficial outcomes associated with small group learning strategies, well-documented problems with small group learning also exist. The social dynamics andorganization of small groups can interfere with learning [60]. It is often assumed that placingstudents in small groups will result in their learning of collaborative skills and teamwork [61],but there is little research that supports this assumption [62]. Girls and boys working in mixedgender small
multivariable control. Dr. Rodriguez has given over 70 invited presentations - 13 plenary - at international and national forums, conferences and corporations. Since 1994, he has directed an extensive engineering mentoring-research academic success and professional development (ASAP) program that has served over 500 students. These efforts have been supported by NSF STEP, S-STEM, and CSEM grants as well as industry. Dr. Rodriguez’ research inter- ests include: control of nonlinear distributed parameter, and sampled-data systems; modeling, simulation, animation, and real-time control (MoSART) of Flexible Autonomous Machines operating in an uncertain Environment (FAME); design and control of micro-air vehicles (MAVs), control