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The teachers in a school have expertise that should be the basis for professional learning focused on what the school needs.
In teaching, the fear of adding “another thing” is quite valid. We know that teaching is not a simple task, yet new initiatives, acronyms, and responsibilities often send the message of a quick fix while adding to teachers’ already stretched schedules.
Teachers don’t need a savior, and they certainly don’t need to be told what to do from the newest and most exciting best-seller. In fact, teachers know what their students need, and it is the job of administrators, coaches, and instructional leaders to facilitate the collection of teachers’ expertise—already present in the school building—to guide staff as they take back the agency of their own professional learning.
As an assistant principal, I’ve found that this approach strikes close to home. Recently, our administrative team prepared a schedule of professional learning opportunities with the intention of placing the power back into teachers’ hands.
Rather than decide what teachers needed, we wanted our teachers to tell us. Using the instructional rounds model, we adapted the process of classroom observations and created a model of peer learning that brought to light areas of instruction that needed improvement—an approach that I will share below.
A Three-Step Peer Learning Process
My school initiates a three-tiered peer learning process to facilitate professional growth: First, we ensure that classroom observations are not evaluative and instead position administrators as facilitators of professional learning days, tasked with organizing teaching coverage and running debrief activities with faculty.
Next, we center problem-solving, not problem-finding. All of our teachers enter classrooms with our school’s current instructional focus in mind. When observing their peers, they look for evidence—in our case, of how feedback loops and competency-based grading impacts our students. They don’t look for a particular teacher’s area of weakness but instead look for evidence of areas in which our building as a whole can improve.
Finally, and most important, we position the observer as the learner, not the expert. Instruction is a deeply personal act for teachers, and many (correctly) see their instruction as an extension of themselves. What teachers give to their students is purposeful, meaningful, and personal, so to have visitors enter a classroom with a holier-than-thou mindset would be to dismantle the entire process of teacher-driven professional learning. Instead, an inquiry mindset invites us all to identify and learn from one another’s strengths.
A Guide to Implementation
After teachers observe three classrooms for 20 minutes each, they come to our debrief session the next morning with their notes. Our administrative team facilitates a reflective conversation that includes the grouping and regrouping of teachers as they discuss what they saw in their hour of classroom visits. In our building, this model allows us to synthesize a collective total of 36 hours of instructional observations.
Specifically, we ask teachers to identify in their observational notes specific pieces of evidence that are connected to our school’s focus. This year, that meant evidence of students collecting feedback, teachers grouping and regrouping students, and teachers mobilizing their grading practices to effectively communicate progress to students.
Each teacher picks their six best pieces of evidence, writes each on a sticky note, and joins a small group to discuss what they saw. These groups determine patterns from the day of rounds and decide the types of professional learning that they feel the building needs to engage in next.
In all, the use of building-based instructional rounds has become the filter through which all of our professional learning opportunities flow. Before any moment of professional development, our team asks the question, “Does this work come directly from what our teachers are seeing during rounds?”
As a result, we have found that the culture around professional learning in our building is shifting: Teachers are more collaborative, are discovering how their strengths often complement another’s weaknesses, and are engaged in and energized by professional learning.
Increasing Teacher Collaboration
After using the instructional rounds process, our teachers lean on each other in new ways; for example, one teacher borrowed a feedback loop that she saw during rounds, realizing that she could adapt a colleague’s approach to coding to fit her students’ work with factoring polynomials.
When we debriefed from our first iteration of rounds in October, four teachers reported that they wanted to emulate the co-teaching model that they observed their colleagues implementing. During our next day of professional learning, teachers already using the method led a short session sharing how they collaboratively planned and executed lessons.
During our second iteration of peer observations in December, another teacher showcased a grouping strategy that put students into three distinct groups based on their performance on a formative task. When teachers saw this process, they decided that the whole building would benefit from professional learning regarding quick ways to collect data and use it to inform approaches to student grouping.
Teachers, in this way, became in-house professional trainers, enhancing whole-school collaboration and uncovering the complementary nature of each other’s strengths and areas for improvement.
Engaging and Energizing Teachers
Like students, teachers yearn for authenticity. They want to know that what they are learning will help their students.
Because instructional rounds allow us to actively determine our needs based on evidence that is collected and processed by our own teachers, we embrace that authenticity and share ownership of our growth. Teachers in our building care about how their colleagues’ lessons go because they observed them, worked alongside them during our professional learning days, and maybe even collaborated with them to experiment with a new instructional move.
These days, there is a real energy in our building as teachers try out new ideas, ask each other to watch a lesson segment outside of organized instructional rounds, and consider the style and content of professional learning that they think will push their practice forward.
A Key Takeaway
When a collaborative approach like instructional rounds is used to organize the professional observation and debrief process, professional learning is deeply meaningful and rooted in the context of a learning community, and—we are finding—more engaging than something that comes from a professional outside of our school walls.
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With a Spotify collaborative playlist, you can team up with your friends to build a big list of your favorite songs. It’s simple to set up a Spotify collaborative playlist but the creator has limited control over who gets to access the playlist so make sure to follow our guide so that disagreements in song picks don’t turn sour.
A collaborative playlist on Spotify can be created on both desktop and the Spotify app so follow the instructions below, depending on what device you use. Make sure to show your friends this page too so that they have guidance on how to add their own songs.
Table of Contents
Spotify is the only streaming service to offer collaborative playlists, but they do have one benefit over the competition. Whilst the creation of Spotify collaborative playlists is exclusive to Spotify premium members, the great thing is that once a playlist has been created, you can share it with anybody and even free Spotify users can add songs and edit the playlist.What Can You Do With a Spotify Collaborative Playlist?
Anybody that has the link for a Spotify collaborative playlist can add songs, change the order of songs in the playlist, and delete songs. Because of this, make sure you only share it with those you want to have access to the playlist. Only the owner of a Spotify collaborative playlist can delete the playlist or change the playlist description, image and title.How To Create a Collaborative Playlist On Spotify Desktop
We are going to assume that you might not already have a playlist ready. If you do, you can skip the first two steps.How To Create a Spotify Collaborative Playlist On Mobile Or Tablet
To create a collaborative playlist on the Spotify mobile app, follow the steps below. If you already have an existing playlist you’d like to switch to collaborative mode, you can skip the first three steps and simply tap on the playlist in your library.
First, tap your library at the bottom of the app.
Next, tap Create playlist on the playlist tab.
Give your playlist a name, and you’ll then be taken to the new playlist page.
On the page for your playlist, tap the three dots in the top right of the display and then tap Make Collaborative.
To share the playlist, tap the three dots again, scroll down and tap share. You can then use any of the sharing options based on the apps you have installed, such as Instagram Stories or Twitter.
Alternatively, tap Copy Link so that you can share it anywhere. Remember, anybody with the link can add, rearrange and delete songs so make sure to share it cautiously.
If you want to add songs to a Spotify collaborative playlist on mobile, make sure you add it to your library first if you’re not the owner. You can do this by tapping the follow button underneath the playlist name. To add any song to a collaborative playlist, tap the three dots on a song, then tap Add to Playlist, then choose the right playlist.
If you want to move the position of songs in your playlist, you can follow the steps below.
Tap your library and then tap the playlists tab.
Find the right playlist and tap it.
On the playlist page, tap the three dots at the top right.
Tap Edit playlist.
Tap and hold your finger on the three stacked lines next to any song you want to move.
Move your finger to move your songs to a new position.
Once finished, make sure to tap save at the top right.Summary
We hope that this guide has shown everything you need to know about creating and using Spotify collaborative playlists.
How can unidentified COVID-19 cases be tracked?
Researchers and provider organisations have increasingly embraced artificial intelligence (AI) and machine learning (ML) tools to reduce and track the spread of COVID-19 and to improve their surveillance efforts. Big data analytics systems have helped health experts to stay ahead of the pandemic from predicting patient outcomes to anticipating future hotspots, resulting in more efficient care delivery. However, the level of pandemic preparation by healthcare organisations is only as good as the data available to them. Although the industry is well aware of the data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery. Nature of the SARS-CoV-2 has led to significant gaps in COVID-19 data with inconsistencies in information, leaving officials uncertain of the effectiveness of public health interventions. “Asymptomatic infections are a common phenomenon in the spread of coronavirus”, said Lucy Li, PhD, a data scientist at the Chan Zuckerberg Biohub. “And it’s very important to understand that phenomenon because depending on how many asymptomatic infections there are, public health interventions might be different.” Chan Zuckerberg Biohub’s researchers are working to cope up with this situation. Li estimated the number of undetected infections using machine learning and cloud computing at 12 locations including Asia, Europe, and the U.S over the course of the pandemic. The results showed that a vast range of infections remained undetected in these parts of the world with the rate of unidentified cases as high as over 90% in Shanghai. Additionally, when the virus was first contracted in these 12 locations, more than 98% of cases were not reported during the first few weeks of the outbreak. This indicates that the pandemic was already well underway by the time intensive testing began. Such findings have crucial implications on public health policy and provider organisations, Lucy Li noted. “For disease outbreaks where you can identify every single infection, rapid testing and a tiny amount of contact tracing is enough to get the epidemic under control, stated Li. “But for coronavirus, there are so many asymptomatic cases out there and testing alone will not help control the pandemic.” “It is because usually when you do testing, you are testing only symptomatic patients which are a subset of the total number of infections out there,” explains Li. “You’re missing a lot of people who are spreading the infection without their knowledge, hence they are not quarantined. Being able to sense of what that number might be is helpful for allocating resources.” Li’s research was backed by AWS Diagnostic Development Initiative which has initiated a global effort to stimulate diagnostic research and innovation during the coronavirus pandemic and to mitigate future disease outbreaks. The data Li is using is viral genomes, the viral DNA. She elaborates, “As the viral genomes spread through the population, they accumulate mutations. These mutations are generally not good or bad; they’re just changes in the genome.” She added, “Every time the virus infects a new individual, it could accumulate new mutations. So, if we know how fast the virus mutates, we can infer how many missing transmission links there were in between the observed genomes.” Li said, “Many different scenarios could explain what we see in the viral genomes. I have to leverage machine learning and cloud computing to test all of those hypotheses and to see which one can explain the observed changes in viral genomes.” She pointed out that these data analytics are well-suited to meet the challenges brought by COVID-19. ML tools allow the researchers to explore different explanations of the data they see so that they can test many hypotheses. With ML and cloud computing technologies, streamlining a previous time-consuming task is possible. By having access to more computational resources in the cloud, time can be reduced from months to days because of the more memory leveraging capacity, which better parallelises analysis. This research may help health officials to monitor the rate of under-reporting in real-time that could indicate how well current surveillance systems are operating. With the available data of COVID-19 pandemic, analytics tools are essential for bringing new insights and potential solutions.
Consuming multiple forms of media simultaneously, like answering an email or scrolling Instagram while watching TV, has become a regular part of daily life. But for the past couple decades, psychologists have been studying the effect this can have on our brains. In a new paper out this week in the journal Nature, researchers suggest that heavy media multitasking affects how well people remember certain events, even those that took place when they weren’t using any technology at all.
The team enrolled 80 participants between the ages of 18 and 26 and about half the study participants were female. Each subject was asked to watch a series of objects flashing across a screen. After a 10-minute break, they watched another series of objects and rated them on criteria like whether they were more or less pleasant than the objects in the first stage as well as bigger or smaller. Included in the second set were objects that had appeared in the first, and one of the criteria that participants were asked to respond to was whether they had seen an image previously.
The participants gave information about their rate of media multitasking, like surfing the internet and watching TV or texting while doing online homework. “We found that heavier media multitasking was related to a shift in how people remembered,” study author Kevin Madore told Popular Science in an email interview. Specifically, participants who identified as heavy media multitaskers misidentified more objects that they had previously seen as new and were more likely to identify new objects as repeats, he said.
During the sessions, participants’ brainwaves and pupil dilation were measured. In psychology, these metrics are commonly used to measure memory and attention. The researchers found heavy media multitaskers suffered a lapse in attention in the moment before they tried to recall how the object in front of them compared to the previous set of objects. To the researchers, this finding suggests that episodic memory—memory of specific past events—was weaker in the heavy media multitaskers, and that heavy media multitaskers have a lower ability to sustain attention.
These findings could have significant consequences. “The basic science implications are important because they offer new answers to why humans sometimes remember and sometimes forget, and why some individuals remember better than others,” Madore wrote to Popular Science.
The implications for human health are also worth noting, he says: “They suggest that there are important interactions among media multitasking, attention, and memory that we should be aware of.”
This finding adds to a growing body of knowledge about the significance of media multitasking in young people. Previous studies have found an association between using multiple forms of media and poor executive function and goal-setting abilities. But there’s a lot we still don’t know about the relationship between this phenomenon and brain function, and researchers also stress the importance of establishing firmer causal relationships between media multitasking and negative health outcomes. “Of equal importance is understanding the types of information processing that are necessary in 21st century learning environments,” one such paper notes.
Zheng Joyce Wang, a professor of communication at The Ohio State University who studies media multitasking, also notes that not all media multitasking is created equal. While the image that comes to mind might be a student watching television while studying, “people taking a class while also searching for relevant concepts on the internet, that’s multitasking too,” she notes.
Wang applauds the study’s approach and desire to look at the longterm impacts of media multitasking, rather than the immediate ones. But she says the metric that researchers used to divide participants into heavy or light multitaskers is “general and overly vague.” Known as the Media Multitasking Inventory, this metric is commonly used in related research but Wang’s lab thinks it needs work.
For this study, she says, “I would be really curious to see if they could take a more refined look at the different types of media multitasking [participants] are doing.”
Young adults and children make up the majority of media multitaskers, and their brains are still developing. We still don’t really understand the consequences of this, although some research has shown changes in brain development related to media multitasking.
But simply abstaining from this activity isn’t an option for young adults, particularly those trying to learn and socialize remotely during a pandemic. Madore notes that the research for this paper was conducted prior to the pandemic, but its findings may be more significant now that even tasks like going to school can involve consuming multiple forms of media at the same time.
Although lower ability to concentrate and shakier episodic memory are both associated with negative academic outcomes and higher impulsivity, Madore writes that it’s hard to know what the longterm consequences of changing memory might be. “We don’t have data that speak to the point about whether the ways our memories operate will fundamentally change living in an always networked world.”
See More : Novel AI Image Generation Guide
The answer is yes, it is possible to generate NSFW content using NovelAI. NovelAI’s text-to-image model enables users to create diverse forms of content, including NSFW imagery. By inputting appropriate prompts, users can guide the AI in generating NSFW visuals that align with their creative intentions.
Age Restriction: Before we explore further, it is crucial to note that NovelAI places an age restriction on its users. To access and utilize the service, individuals must be 18 years or older. This age restriction emphasizes the need for responsible usage and ensures compliance with legal and ethical guidelines.
NovelAI incorporates an “Unwanted Content” filter that automatically removes low-quality and inappropriate content. This filter helps maintain the integrity of the generated outputs and ensures a safer and more suitable user experience. However, it is essential to remember that the inclusion of the “NSFW” tag removes the content from the filter.
When it comes to generating NSFW content using NovelAI or any other AI generator, ethical considerations should be paramount. Users must exercise responsible behavior and be mindful of the impact their creations may have on others. Respecting consent, privacy, and societal norms is crucial to avoid any potential harm or misuse.
Also Read : Is NovelAI Down? Get Latest Updates!
To generate NSFW content using NovelAI, users need to add the “NSFW” tag to their prompts. By incorporating this tag, the AI model understands the intended nature of the content and generates visuals that align with the NSFW category.
It’s important to note that NovelAI provides users with the freedom to explore their creative boundaries while using the platform. However, users must exercise caution and responsibility when generating NSFW content. Respecting legal and ethical guidelines ensures a positive experience for both creators and consumers.
An active community called r/nsfwNovelai exists on Reddit, dedicated to sharing NSFW content created using NovelAI. This community provides a space for creators to showcase their work, discuss techniques, and engage in conversations related to NSFW content generation. However, it’s crucial to remember that responsible usage and adherence to ethical standards are paramount within this community as well.
Q. Is it possible to generate NSFW content using NovelAI?
Yes, NovelAI’s text-to-image model allows the creation of NSFW content.
Q. What are the limitations when generating NSFW content with NovelAI?
NovelAI has an age restriction, requiring users to be 18 years or older.
The platform includes an “Unwanted Content” filter that removes low-quality and inappropriate content. Adding the “NSFW” tag removes the content from this filter.
Q. How should NSFW content generation using NovelAI be approached?
Users must approach NSFW content generation ethically and responsibly, respecting consent, privacy, and societal norms.
Q. Can users share NSFW content created on NovelAI?
Yes, there is a Reddit community called r/nsfwNovelai dedicated to sharing NSFW content generated on the platform.
Q. Are there any guidelines for generating NSFW content on NovelAI?
While NovelAI allows creative freedom, users should adhere to legal and ethical guidelines to ensure responsible content creation.
Q. What precautions should be taken when creating NSFW content?
Users should exercise caution, respect consent, and consider potential implications or harm that may arise from the content they create.
NovelAI’s text-to-image model offers the possibility of generating NSFW content. However, responsible usage, ethical considerations, and adherence to legal guidelines are of utmost importance. Users must exercise caution, respect consent, and prioritize the well-being of others when creating and sharing NSFW content. By approaching the generation of NSFW content with NovelAI ethically and responsibly, we can harness the potential of AI in a positive and inclusive manner.
People often ask me what evidence there is to support the view that our schools should promote social, emotional, and character development in our students. They seem especially interested in whether SECD actually helps shape the character and behavior of students over time.
Because many educators lose track of former students as they move on in grades and grow into adulthood, it’s natural for them to wonder, “Did the SECD curriculum really help my students and do any good in their lives?” A recent study published by the research team at the Seattle Social Development Project reminds us that, when delivered effectively, SECD interventions in schools have long-term benefits.The Results of an Earlier Study
The Rutgers Social-Emotional Learning Lab, of which I am director, carried out one of the earliest peer-reviewed studies on the benefits of SECD. In the study, the group compared three cohorts of students who received social decision-making/social-problem-solving (SDM/SPS) lessons in elementary school. The groups received varying amounts of the program’s components, ranging from two years to five years, with some follow-up in high school after concluding the intervention. Educators used students who received no treatment as a control group.
Results from this study indicate that ninth-grade students who had received interventions drank significantly less alcohol, had fewer self-destructive or identity problems, earned higher scores in overall social competence, exhibited a higher level of membership and participation in positive social organizations and nonsports activities, and did better on-the-job work.
Tenth-grade students who hadn’t participated, meanwhile, had significantly higher rates of vandalizing school property, attacking persons with intent to injure, hitting or threatening other students, self-destructive or identity problems, and unpopularity than students who went through the program. They also showed lower scores in overall social competence. Eleventh-grade students in the control group had significantly higher rates of vandalizing their parent’s property, hitting or threatening their parents, and using chewing tobacco than students in the program.
Across grades, male students in the control groups significantly exceeded male students in the program in rates of petty theft and buying alcohol. The findings also indicated that students who were in the higher-fidelity program generally showed better goal attainment than those in the lower-fidelity program.Further Findings
David Hawkins, founding director of the University of Washington’s Social Development Research Group, says that fifteen years after the Seattle Social Development Program conducted its evidence-based SECD intervention, young adults ages twenty-four and twenty-seven who were part of the intervention reported better mental and sexual health and higher educational and economic achievement than a control group of young adults who didn’t receive the intervention.
As lead author of the study, Hawkins told Science Daily in a recent interview, “The effects of working with children in elementary school show up in their teen years as their rates of violence, heavy alcohol use, and dropping out of school are reduced. By age twenty-one, more of them have completed high school and have better jobs. And by ages twenty-four and twenty-seven, they are above the median in socioeconomic status and education, and they are having fewer mental-health and sexual-health problems.”
The study involved 598 students from fifteen Seattle public schools that serve high-crime neighborhoods. The participants were divided into three groups. One group of 146 students received the intervention in grades 1-6. A second group of 251 students received a partial intervention in only grades 5-6. And the third group of 201 students did not receive any training from the program.
Hawkins reported that the dosage effect found in the SDM/SPS program — and in earlier studies of the Seattle program — was still evident. Children who received the full intervention in elementary school showed the strongest effects and the most positive functioning when followed up. Those receiving the partial intervention showed lesser effects, though they were generally better than the no-exposure control group. The findings indicate that those who received the full intervention had significantly fewer sexually transmitted diseases and reported higher income, increased responsibilities at work, and more community involvement. However, the full intervention had no effect on reducing substance abuse or cutting criminal behavior in young adulthood.
Said Hawkins, “The real value in following people over time is that we get to see how what we do in childhood affects their lives and has enduring effects as they change. We can’t know how one phase of development affects the next step unless we follow people over time.”The Power of SECD
These studies complement other data — from a meta-analysis completed recently by the Collaborative for Academic, Social, and Emotional Learning — that reveal the positive follow-up benefits of SECD programs for students in both academics and mental health. The consistent message of these studies, however, is that the “dose” matters and that comprehensive, coordinated, multiyear efforts at SECD are what yield positive results.
This is why my organization, Developing Safe and Civil Schools, is working with New Jersey schools to ensure that they are carrying out their programs in problem prevention, promotion of social and emotional competence, positive youth and character development, and school-climate improvement in ways that will yield the desired effects. Too many schools are doing more than they need to, but with not enough efficiency and coordination to achieve the desired academic and behavioral outcomes. The evidence suggests that we can do better without doing much more.
What do you think about SECD efforts in public schools? Please share your thoughts.
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