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Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing\(27 \mathrm{kg}\)without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification werequickly,smoothlyandavoiding obstacles. Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team. 

Concurrent enrollment enables high school teachers approved by a partnering college or university to teach college-level coursework to their students. The collaborative research-practice partnership project CS-through-CE examines if and how concurrent enrollment (CE) programs can effectively broaden participation in computing for secondary students. In the CS-through-CE project two participating higher education institutions - Capital Community College (CCC) in Hartford, CT, and Southwest Minnesota State University (SMSU) in Marshall, MN - collaborated with the Mobile Computer Science Principles (CSP) team to train secondary teachers to teach the Mobile CSP course, and then offer the Mobile CSP course as a CE course. In this experience paper, faculty from CCC and SMSU detail their experiences recruiting secondary partners to teach Mobile CSP as a CE course, including the barriers and challenges encountered and the strategies identified for overcoming them. Additionally, participating secondary instructors from Hartford Trinity Magnet College Academy in Hartford, CT and Northeast Range School in Babbit, MN detail their experiences teaching Mobile CSP as a CE course in their high schools. They share their experiences teaching Mobile CSP as a CE course, contrast this experience to teaching the course in an Advanced Placement (AP) format, and detail the benefits they see in each modality. The experiences of the college faculty and secondary instructors in this paper are informative for any secondary or post-secondary educator interested in cultivating or expanding pathways in CS through concurrent enrollment. 

Most U.S. states support college-readiness and access through dual enrollment, in which high school students enroll in college courses. Concurrent enrollment (CE) allows students to take college courses in their own high school, taught by high school teachers approved by the partner college. CE has positive effects on students' education, but rarely is CS available through CE. Unlike AP, CE provides college credit to students who are assessed throughout the course rather than by a single high-stakes exam/project. This panel will showcase four different types of post-secondary institutions' experiences offering CS-through-CE and discuss its potential as an entry point into CS for students underrepresented in computing, including those in urban and rural settings. Panelists will share challenges (such as teacher credentialing) and benefits of CS-through-CE. The audience will understand supports and barriers to creating CS-through-CE courses, will be provided with resources, and will crowd-source possible next steps in implementing CE as a model for broadening participation.