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Detecting/Preventing Infections, and Moving Instruction Online


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Terrence DeFranco, Jeremy Roschelle

https://bit.ly/39DcsOb March 17, 2020

As of March 17th, 2020, more than 188,297 people have been infected with COVID-19. How can technology aid in curtailing the spread of infectious diseases that have the potential to create panic and infirm thousands of people? The Internet of Things (IoT), a network of interconnected systems and advances in data analytics, artificial intelligence, and connectivity, can help by providing an early warning system to curb the spread of infectious diseases.

China's efforts to control the coronavirus have meant many residents stayed at home and factories just shut down. That had an unintended effect: less air pollution. Cleaner air can improve public health, maybe even save lives. Indoor air pollution has been ranked among the top five environmental risks to public health, and the advent of COVID-19 puts a spotlight on the need to ensure we can remove volatile indoor contaminants.

Leveraging IoT in in our indoor environments could help prevent highly infectious diseases from spreading rapidly in today's global world. Typically, efforts to improve the environment tend to focus on the outdoors. According to the U.S. Environmental Protection Agency (EPA), indoor air in homes and buildings may be more polluted than outdoor air—a serious issue since people spend, on average, 90% of their time indoors. With the advent of COVID-19, we are mandated to remain indoors, further highlighting the need to ensure that our indoor air quality is good.

LEED (Leadership in Energy and Environmental Design) is an internationally recognized green building certification system aimed at improving performance across all the metrics that matter most: energy savings, water efficiency, CO2 emissions reduction, improved indoor environmental quality, and stewardship of resources. LEED places emphasis on indoor environmental quality because poor air quality negatively impacts occupant health and safety.

According to the EPA, poor indoor air quality affects 33% to 50% of commercial buildings in the U.S., sometimes causing "sick building" syndrome, which causes a wide variety of symptoms. Sick building syndrome could be caused by inadequate ventilation (the introduction and distribution of clean air); biological contaminants such as molds, bacteria, and viruses; or chemical contaminants like volatile organic compounds or formaldehyde.

To create a healthy and comfortable indoor environment, business leaders should implement an indoor air quality management program that both controls contaminants and ensures adequate ventilation. The foundation of a good in-door air quality program is an Internet of Things (IoT) platform that monitors the air continuously to detect the presence of common pollutants and helps maintain the appropriate volume of fresh air. It also provides actionable data you can use to address existing issues and document your improvement over time.

In terms of looking future forward, IoT may be leveraged to more quickly detect infection or deliver care more efficiently. These scenarios may include a network of advanced indoor sensor technology such as virus-detection sensors to detect and remediate the presence of toxins. Additionally, home telemedicine technology using wearable technology sensors can monitor health conditions of affected patients and keep them closely linked via this medium with their health care provider rather than physical visits, which would have the effect of reducing the burden to the healthcare system and reduce human interaction. Affected patients can be closely monitored via telemedicine to reduce the burden on healthcare facilities.

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Jeremy Roschelle: Powerful Online Learning is a Distributed System

https://bit.ly/2UzlwPI March 25, 2020

In the midst of a pandemic, universities are now rapidly shifting to online and remote learning. I will suggest a metaphor for powerful online learning. This metaphor should resonate with our backgrounds as computer ccientists and it also fits core principles of the learning sciences. As this is my inaugural blog in this venue, let introduce myself: I'm a learning scientist who works on improving STEM and CS learning with technology. I also have a CS degree from MIT. I like to connect the computer science and learning sciences parts of my brain.

Let's start with what good online learning is not: it is not moving lectures online and continuing business as usual. Stated in computational terms, it is not about a central computational process (the instructor) doling out the same unit of work to hundreds or thousands of processors (the students) and then providing an authoritative rating of each processor's individual work.

Let's consider this alternative metaphor: designing powerful online learning as designing an effective distributed processing system. Stated in computational terms, it is about coordinating the active, engaged local work of separate processors (the students) toward a common goal (a community with greater shared understanding of the subject matter). It's about organizing the connectivity in the system so processors (students) give each other help and feedback, and thus converge toward better learning.

Why active, engaged work? Because the direct cause of learning is not instruction, but rather the active, engaged effort a student commits to making sense of a challenging concept in their zone of proximal development (their area of growth).

Why coordinating? Because learning sciences has collected a large body of evidence that when students have to elaborate their knowledge for another student (or coordinate on a shared knowledge product), they learn more. Students also learn more when they provide feedback to each other with explanations, not just answers or "do it like this." Collaborative learning is very powerful when roles are structured so that students are required to actively elaborate, coordinate, and give feedback. Together, active learning and collaborative learning expand the "zone" for learning.

In a project called GroupScribbles, we took the distributed systems metaphor literally. We created a "blackboard architecture" for a classroom. In GroupScribbles, individual processors (students) take jobs (intellectual work) from a shared space, and post partial results (learning) back to the shared space. The taking and posting of work is mediated by virtual sticky notes that students move between their own private space and a public space.

It's much simpler than it sounds. In the GroupScribbles system pictured above, an elementary school teacher created initial sticky notes that asked for representation of a particular fraction. Students selected a fraction to work on by taking a blank note from the shared space to their private space (the atomic "take" operation in a blackboard architecture). They posted back a drawing of the fraction (the put operation in a blackboard architecture).

Then small groups were asked to take a collection of stickies for one fraction to a separate room. In the room, they elaborated an explanation for each representation, and then chose a single best explanation to share with the whole class. Finally, the teacher led a discussion of the smaller set of student work that was shared back. This is a powerful learning activity because students are working hard to elaborate, coordinate, and get feedback on what they understand across multiple modes: as individuals, in small groups, and in larger discussions.

Notice who is doing the work that drives learning in this distributed system: the students. Notice the instructor's role: to find a creative way to make every student think hard about a different facet of the same problem, to use small groups to encourage knowledge coordination and feedback among students, and then to regulate the flow of information back to the central blackboard, where a teacher can add their unique value in further discussing some of the selected work of the students.

This metaphor can be fruitfully extended:

Think of students as a heterogeneous set of differently-abled processors. How can an instructor create a coordinated system so that every processor works as hard as they can? How can the cognitive diversity of students become an asset that drives learning to become deeper?

An instructor might consider how students can give peer feedback; how students can self-select easier or harder challenges; how students can promote issues or questions from small groups to bigger groups; how shared understanding of a concept can be made stronger by comparing and contrasting different student elaborations of the same concept. In an elegantly architected distributed system, the connectivity and activity of individual processors (students) overcomes the limited bandwidth of the central processor to give attention to each individual unit.

The Distributed Systems Metaphor for Online Learning suggests that an instructor should fully engage and connect the students (the processing nodes) to maximize their active effort to elaborate, coordinate, and give each other constructive feedback, with a collective goal in mind (that is, learning more deeply by harnessing cognitive diversity).

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Authors

Terrence DeFranco is CEO of IOTA Communications, the first IoT network employing FCC licensed spectrum, based in Allentown, PA, USA.

Jeremy Roschelle is Executive Director of Learning Sciences Research at Digital Promise and a Fellow of the International Society of the Learning Sciences, Bloomington, IN, USA.


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