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Students in an 8 a.m. principles of data management class talk among each other as they solve a data base problem. Sometimes they get up and write on a 6-foot whiteboard on wheels that doubles as a screen between tables of students in their extra-large classroom.

The room in Slaughter Hall seats 150 students and is meant to simulate the learning spaces in the Student Hall for Exploration and Development (SHED) that will hold classes next fall. The Slaughter classroom, dubbed the betaSHED, combines three rooms to give professors and students a preview of the large-scale learning environment.

Emanuel Griffin, a third-year computer science major from Brooklyn, N.Y., said its hard to learn when a professor stands in front of the room and lectures or reads from their Power Point slides.

Here its really different because you take the material and then use it on the problem, Griffin said. You also collaborate with classmates and get feedback and learn how to do it properly. I like it a lot.

Instead of lecture-hall seating, the students sit at tables of six and function as their own cohort. Professors and teaching assistants circle through the classroom answering questions.

You can feel how many people are here, but its also large enough that you dont feel cramped, said Jackson Connor, a third-year computer science major from Lancaster, N.Y. Its a nice change of pace from other classes.

The betaSHED classroom combines three sections of the same computer science class. Next fall, five large format, active-learning classrooms in the SHED will combine some multi-section classes and seat up to 150 students. Projectors, screens, and other learning technologies will enhance these spaces.

Jeremy Brown 01 (computer science) and Scott Johnson 14 (computer science) 14 MS (computer science), senior lecturers in the Department of Computer Science, co-teach the principles of data management class in the practice SHED classroom. They had long wanted to flip their classroomsputting lectures on videos and focusing on activities and group work during classtime. It wasnt until the COVID pandemic forced educators to rethink how they deliver information that Brown and Johnson created their video lectures and began creating a library of activities.

The activities are tricky to create. They must challenge students without discouraging them and fill the class time. We have spent many semesters rewriting and revamping activities that ask students to do more than look up answers in a textbook, Brown said.

Active learning means a lot of different things, Johnson said. For us, it's more about engaging the students with each other. Instead of the students just sitting in lecture, being bored and taking notes, they are now doing lecture outside of class and then coming in and consulting with their peers, he said. They teach each other.

This style of teaching can reduce failure rates, improve grades and attendance, create community among students, and allow for more professor-student interaction. It is also inclusive. Deaf and hard-of-hearing students in these cohorts face their peers and the sign language interpreter sits at the table with them, Johnson said.

The dynamic in Johnson and Browns data management class creates an upbeat vibe in the room.

I can see what the positive impacts are with everyone in the same space and having more people together, and the collaborative energy, said Uzo Ukekwe, a third-year computer science major, from Monroe, N.Y.

Key to active learningespecially in large format classesis the prep work that takes place ahead of time. For Michelle Weatherell 04 (biology) 12 MS (environmental science), lecturer in the Thomas H. Gosnell School of Life Sciences, this means working with the general biology lecture instructor team, which includes Sandi Connelly and Emily Coon-Frisch, to plan activities and problem-solve the logistics of preparing and transporting materials needed for their large sections of general biology. This semester, Weatherell, who teaches in the betaSHED room, uses two teaching assistants to enhance learning in the large classroom.

The students, in my opinion, seem to be more confident and comfortable with the material, Weatherell said. I also feel that more students are asking questions above the material, and applying it outside of what we are doing.

Weatherell encourages faculty who are curious about active learning to observe one of the classes in the large room.

It's interesting to see how instructors are using this space because everyone interprets active learning a little bit differently, Weatherell said.

Brown and Johnson have already booked classtime in the betaSHED room for the spring semester and are eager to teach in the SHED when it opens in fall 2023. They have seen the benefits of active learning and apply elements of it in every class they teach. Students with different learning styles appreciate the difference.

Nate Mount, a second-year computer science major from Westminster, Md., said the small group projects keep him focused. Here the professors are able to move around and check you along with the TAs and it keeps you more in the mindset of the material youre looking into that day.

Courses that lend themselves to active learning can help students get the most out of a class, Brown observed.

Students are changing, Brown said. They arent used to being lectured to for hours on end. Active learning is about getting them engaged.

Workshops for active learning

Workshops for RIT faculty, adjunct faculty, and teaching assistants will share strategies for adopting active learning in regular-sized classes and for the extra-large learning spaces that will be available next fall in the Student Hall for Exploration and Development. National expert Andy Gerhart, professor of mechanical, robotics, and industrial engineering at Lawrence Technological University and facilitator for KEEN (the Kern Entrepreneurial Engineering Network), will share his best practices for facilitating learning through small group collaborations. Registration is required to attend the free, in-person workshops:

The Center for Teaching and Learning is sponsoring the workshops, following a remote session Gerhart led via Zoom in September. Contact Sandi Connelly, interim associate director, at sjcsbi@rit.edu to view the recording of the earlier session.

To learn more about teaching in the SHED, go to TeachSHED.

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RIT faculty prepare to teach large classes in SHED using scaled-up classroom | College of Science | RIT - Rochester Institute of Technology

Go Back For Immediate Release 10.11.2022 Governor Hutchinson Selected to Lead National Education Organization

LITTLE ROCK Governor Asa Hutchinson has been selected as Chairman of the Education Commission of the States (ECS),a national, nonpartisan organization that serves as a partner to state education leaders.

"It is an honor to have been selected as Chairman of ECS," Governor Hutchinson said. "Arkansas has been a leader in education from computer science to pandemic learning, and I am excited for the opportunity to work with other leaders to secure our children's future."

During his time as Chairman, Governor Hutchinson will lead the Chair's Initiative, which will focus on expanded computer science education.

The Governor's selection comes on the heels of his recent tenure as National Governors Association Chairman, where he led the most successful Chair's Initiative in the organization's history. The Compact to ExpandK-12 Computer Science Education was signed by 50 state and territorial governors, the most ever for a Chairman's Initiative.

ECS' team of experts provides trusted information and opportunities for partnership that allow policymakers to gain the insight and experience needed to create effective education policy for their states.

You can find more information on the Education Commission of the StatesHERE.

CONTACT:Press Shop (press@governor.arkansas.gov)

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Governor Hutchinson Selected to Lead National Education Organization - Governor Asa Hutchinson

Photo by Russell Cothren

Khoa Luu and Ashley Dowling monitor insect activity at the U of A Farm.

The U of A and U of A System Division of Agriculture researchers have developed a prototype of an insect trap that can help farmers monitor and identify potential pests more efficiently.

Ashley Dowling, a professor of entomology and plant pathology conducting research for the Arkansas Agricultural Experiment Station, partnered with Khoa Luu, an assistant professor of computer science and computer engineering, to create a trap that captures footage of insects, uses artificial intelligence to identify them and sends real-time data back to the farmers.

Once farmers become aware that certain pests are present, they can take the appropriate measures to counteract their potential damage.

The trap itself was created by a local Arkansas business [SolaRid AR], and they came to the university with lots of ideas about what theyd like their trap to be able to do, Dowling said. One of the things on their list was this smart trap approach to things. We jumped at the chance to work with them and create this for them.

Prior to the development of this prototype, farmers typically set traps manually and then sent someone out to evaluate them every few days a process that takes much longer and can sometimes result in crops being ruined in the meantime.

Oftentimes, by the time they get the data, its almost too late, Dowling says.

The new device, however, eliminates the need for manual monitoring, so farmers can make decisions on the fly.

The trap itself is attracting insects using lights of certain wavelengths that are attractive to insects, Dowling explains. It also has the ability to put chemical odors into it that you can target very specific insects with.

As insects beeline their way into the trap, they pass through a sensor with an infrared plane, which then activates the camera. From here, the computer uses artificial intelligence perfected by Luu to identify the insect and transmit the results to the user.

Dowling explains that when certain thresholds of insects are reached in a field, that tells the farmer its time to take action, particularly to avoid substantial economic losses. And, knowing if the pests are isolated to a certain area allows farmers to target only the areas that are potentially affected.

Beyond the fields, the technology has the capability of being applied in other areas of entomology, like biodiversity or museum collections, where samples containing thousands of specimens are regularly collected and need to be analyzed in a timely manner.

Learn more about Dowling and Luus smart insect monitoring system inCaught on Camera: Insects Editionand find additionalShort Takesvideos here.

About the University of Arkansas:As Arkansas' flagship institution, theUofAprovides an internationally competitive education in more than 200 academic programs. Founded in 1871, theUofAcontributes more than$2.2 billion to Arkansas economythrough the teaching of new knowledge and skills, entrepreneurship and job development, discovery through research and creative activity while also providing training for professional disciplines. The Carnegie Foundation classifies theUofAamong the few U.S. colleges and universities with the highest level of research activity.U.S. News & World Reportranks theUofAamong the top public universities in the nation. See how theUofAworks to build a better world atArkansas Research News.

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Insects Get 'Caught on Camera' to Help Farmers in Latest Short Takes - University of Arkansas Newswire

A growing number of people are living with conditions that could benefit from physical rehabilitation but there arent enough physical therapists (PTs) to go around. The growing need for PTs is racing alongside population growth, and aging, as well as higher rates of severe ailments, are contributing to the problem.

An upsurge in sensor-based techniques, such as on-body motion sensors, has provided some autonomy and precision for patients who could benefit from robotic systems to supplement human therapists. Still, the minimalist watches and rings that are currently available largely rely on motion data, which lack more holistic data a physical therapist pieces together, including muscle engagement and tension, in addition to movement.

This muscle-motion language barrier recently prompted the creation of an unsupervised physical rehabilitation system, MuscleRehab, by researchers from MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital. There are three ingredients: motion tracking that captures motion activity, an imaging technique called electrical impedance tomography (EIT) that measures what the muscles are up to, and a virtual reality (VR) headset and tracking suit that lets a patient watch themselves perform alongside a physical therapist.

Patients put on the sleek ninja-esque all-black tracking suit and then perform various exercises such as lunges, knee bends, dead lifts, leg raises, knee extensions, squats, fire hydrants, and bridges that measure activity of quadriceps, sartorius, hamstrings, and abductors.VR captures 3D movement data.

In the virtual environment, patients are given two conditions. In both cases, their avatar performs alongside a physical therapist. In the first situation, just the motion tracking data is overlaid onto their patient avatar. In the second situation, the patient puts on the EIT sensing straps, and then they have all the information of the motion and muscle engagement.

With these two conditions, the team compared the exercise accuracy and handed the results to a professional therapist, who explained which muscle groups were supposed to be engaged during each of the exercises. By visualizing both muscle engagement and motion data during these unsupervised exercises instead of just motion alone, the overall accuracy of exercises improved by 15 percent.

The team then did a cross-comparison of how much time during the exercises the correct muscle group got triggered between the two conditions. In the condition where they show the muscle engagement data in real-time, that's the feedback. By monitoring and recording the most engagement data, the PTs reported a much better understanding of the quality of the patient's exercise, and that it helped to better evaluate their current regime and exercise based on those stats.

We wanted our sensing scenario to not be limited to a clinical setting, to better enable data-driven unsupervised rehabilitation for athletes in injury recovery, patients currently in physical therapy, or those with physical limiting ailments, to ultimately see if we can assist with not only recovery, but perhaps prevention, says Junyi Zhu, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author on a new paper about MuscleRehab. By actively measuring deep muscle engagement, we can observe if the data is abnormal compared to a patient's baseline, to provide insight into the potential muscle trajectory.

Current sensing technologies focus mostly on tracking behaviors and heart rates, but Zhu was interested in finding a better way than electromyography (EMG) to sense the engagement (blood flow, stretching, contracting) of different layers of the muscles. EMG only captures muscle activity right beneath the skin, unless its done invasively.

Zhu has been digging into the realm of personal health-sensing devices for some time now. Hed been inspired by using EIT, which measures electrical conductivity of muscles, for his project in 2021 that used the noninvasive imaging technique to create a toolkit for designing and fabricating health and motion sensing devices. To his knowledge, EIT, which is usually used for monitoring lung function, detecting chest tumors, and diagnosing pulmonary embolism, hadnt been done before.

With MuscleRehab, the EIT sensing board serves as the brains behind the system. Its accompanied by two straps filled with electrodes that are slipped onto a users upper thigh to capture 3D volumetric data. The motion capturing process uses 39 markers and a number of cameras that sense very high frame rates per second. The EIT sensing data shows actively triggered muscles highlighted on the display, and a given muscle becomes darker with more engagement.

Currently, MuscleRehab focuses on the upper thigh and the major muscle groups inside, but down the line theyd like to expand to the glutes. The team is also exploring potential avenues in using EIT in radiotherapy in collaboration with Piotr Zygmanski, medical physicist at the Brigham and Womens Hospital and Dana-Farber Cancer Institute and Associate Professor of Radiation at Harvard Medical School.

We are exploring utilization of electrical fields and currents for detection of radiation as well as for imaging of the of dielectric properties of patient anatomy during radiotherapy treatment, or as a result of the treatment, says Zygmanski. Radiation induces currents inside tissues and cells and other media for instance, detectors in addition to making direct damage at the molecular level (DNA damage). We have found the EIT instrumentation developed by the MIT team to be particularly suitable for exploring such novel applications of EIT in radiotherapy. We are hoping that with the customization of the electronic parameters of the EIT system we can achieve these goals.

This work advances EIT, a sensing approach conventionally used in clinical settings, with an ingenious and unique combination with virtual reality, says Yang Zhang, assistant professor in electrical and computer engineering at the UCLA Samueli School of Engineering, who was not involved in the paper. The enabled application that facilitates rehabilitation potentially has a wide impact across society to help patients conduct physical rehabilitation safely and effectively at home. Such tools to eliminate the need for clinical resources and personnel have long been needed for the lack of workforce in healthcare.

The papers MIT co-authors are graduate students Yuxuan Lei and Gila Schein, MIT undergraduate student Aashini Shah, and MIT Professor Stefanie Mueller, all CSAIL affiliates. Other authors are Hamid Ghaednia, instructor at the Department of Orthopaedic Surgery of Harvard Medical School and co-director of Center for Physical Artificial Intelligence at Mass General Hospital; Joseph Schwab, chief of the Orthopaedic Spine Center, director of spine oncology, co-director of the Stephan L. Harris Chordoma Center, and associate professor of orthopedic surgery at Harvard Medical School; as well as Casper Harteveld, associate dean and professor at Northeastern University. They will present the paper at The ACM Symposium on User Interface Software and Technology later this month.

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MIT system sees the inner structure of the body during physical rehab - MIT News