Search

Robots have the potential to save lives in emergency scenarios, but could have an equally disastrous effect if participants overtrust them. To explore this concept, we performed an experiment where a participant interacts with a robot in a non-emergency task to experience its behavior and then chooses whether to follow the robot's instructions in an emergency or not. Artificial smoke and fire alarms were used to add a sense of urgency. To our surprise, all 26 participants followed the robot in the emergency, despite half observing the same robot perform poorly in a navigation guidance task just minutes before. We performed additional exploratory studies investigating different failure modes. Even when the robot pointed to a dark room with no discernible exit the majority of people did not choose to safely exit the way they entered. [ABSTRACT FROM AUTHOR]

The Summer Undergraduate Research in Engineering/Science (SURE) program, initiated in 1992, is a ten-week summer program for junior and senior level undergraduates from U.S. institutions. In 2013, the program instituted a new initiative to provide opportunities to students focused on robotics research, and as a direct consequence, interest them in opportunities available through graduate study. Robotics, as a discipline, is inherently interdisciplinary, combining all aspects of engineering and computer science necessary for designing and deploying integrated systems and solutions. Every year, eight students with diverse backgrounds are selected and paired with faculty advisors and graduate student mentors who are members of the Institute for Robotics and Intelligent Machines. In addition to conducting research during their stay, students also participate in a week-long robotics boot camp their first week, attend weekly seminars on emerging research in engineering fields, visit local industry, participate in enrichment and academic development activities, and attend social events. Students conclude the program with research presentations to their peers and faculty and graduate student mentors. During the life of the SURE program, 541 students, selected from a pool of 2,899 applicants, have participated in the program. A comprehensive assessment program for SURE has been developed and implemented. The assessment process is driven by the overall program objective to provide participants a meaningful research experience and enrichment activities to increase the likelihood that participants will attend graduate school in engineering/science. This model targets three cohorts from which data is collected. Each data source provides unique information that contributes to a comprehensive analysis of the impact and experiences of program participants and to an understanding of the academic trends of all SURE program applicants. The pre- and post-program surveys, focus group session and interviews with participants include a series of questions about students' research interests, and participants' perceived impact of SURE on research skills and planned graduate school attendance. The faculty advisor survey includes questions about the contribution the students made to their research programs, whether or not their student would succeed in graduate school, the quality of the students' oral and written project presentations, likelihood of future collaboration with the student and how the program could be improved in future years. The Longitudinal Survey of Former SURE Participants, conducted every four years, addresses the primary objective of the SURE program to motivate participating students to attend graduate school in engineering/science. Respondents are asked a variety of questions about their academic decisions after participating in the SURE program. Detailed data about graduate school attendance, degree attainment, and major is collected and analyzed. Questions are included to obtain feedback about their co-curricular activities and the environment of their undergraduate institutions. Another set of questions refer to sources of encouragement that students might have had when deciding to attend graduate school. Thus far, the program has been tremendously successful in attaining its primary objective. Collected outcome measures have shown that 76% of the students who participate in the program attend graduate school in engineering/science and 6% attend medical school upon receiving their B.S. degree. [ABSTRACT FROM AUTHOR]

The rapid advancement of low cost embedded systems invites engineering educators to deliver content and skills while engaging students in hands-on projects that may potentially make use of a variety of platforms. From proprietary to open source hardware and software, from microcontrollers to embedded operating systems (OS) devices, students today are challenged to accelerate their learning pace by working on real-world projects in a supervised environment. Currently, students rely on self-guidance in order to develop the knowledge base necessary for achieving the goals of their team projects with respect to embedded systems. This paper presents the results of an embedded systems programming tutorial piloted with a group of 15 undergraduate students from across the engineering disciplines and with a mix of sophomores through seniors. The tutorial included a series of three lecture modules prepared to introduce concepts on embedded systems. The modules were tightly coupled with hands-on exercises that employed the DE2i-150 development board, aimed to introduce students to the development of digital systems based on an Altera field programmable gate array (FPGA) in conjunction with an Linux-based embedded OS running on an Intel Atom processor. The first module provides an overview of the various inputs, outputs, sensors, and displays of the board. The second module introduces the embedded OS and guides students in establishing a high-speed Peripheral Component Interconnect Express (PCIe) link between the Atom processor and the FPGA. The third module focuses on implementing a custom C/++ application written by students for real-time processing of inputs from four push buttons. Results show that the three lecture modules of 30 minutes each, coupled with the hands-on projects, offer a good introduction to the complexity of the DE2i-150 board. Surveys collected before the first module and after the third show a generally positive outcome. Recommendations for future efforts focus on incorporating more complex C/++ applications concurrent with FPGA development. [ABSTRACT FROM AUTHOR]

Adaptive learning is an educational method that utilizes computers as an interactive teaching device. Intelligent tutoring systems, or educational agents, use adaptive learning techniques to adapt to each student's needs and learning styles in order to individualize learning. Effective educational agents should accomplish two essential goals during the learning process-1) monitor engagement of the student during the interaction and 2) apply behavioral strategies to maintain the student's attention when engagement decreases. In this paper, we focus on the first objective of monitoring student engagement. Most educational agents do not monitor engagement explicitly, but rather assume engagement and adapt their interaction based on the student's responses to questions and tasks. A few advanced methods have begun to incorporate models of engagement through vision-based algorithms that assess behavioral cues such as eye gaze, head pose, gestures, and facial expressions. Unfortunately, these methods require a heavy computation load, memory/storage constraints, and high power consumption. In addition, these behavioral cues do not correlate well with achievement of high-cognitive tasks, as we will discuss in this paper. As an alternative, our proposed model of engagement uses physical events, such as keyboard and mouse events. This approach requires fewer resources and lower power consumption, which is also ideally suited for mobile educational agents such as handheld tablets and robotic platforms. In this paper, we discuss our engagement model which uses techniques that determine behavioral user state and correlate these findings to mouse and keyboard events. In particular, we observe three event processes: total time required to answer a question; accuracy of responses; and proper function executions. We evaluate the correctness of our model based on an investigation involving a middle-school after-school program in which a 15-question math exam that varies in cognitive difficulty is used for assessment. Eye gaze and head pose techniques are referenced for the baseline metric of engagement. We then conclude the investigation with a survey to gather the subject's perspective of their mental state throughout the exam. We found that our model of engagement is comparable to the eye gaze and head pose techniques. When high-level cognitive thinking is required, our model is more accurate than the eye gaze and head pose techniques due to the use of outside variables for assistance and non-focused gazes during questions requiring deep thought. The large time delay associated with the lack of eye contact between the student and the computer screen causes the aforementioned algorithms to incorrectly declare the subjects as being disengaged. Furthermore, speed and validity of responses can help to determine how well the student understands the material, and this is confirmed through the survey responses and video observations. This additional information will be used in the future to better integrate instructional scaffolding and adaptation with the educational agent. [ABSTRACT FROM AUTHOR]