Screencastify is a tool that allows students and educators to personalize their learning experience through sharing their voice via a screen recording. The app is a Chrome extension, meaning the tool is always at the ready whenever you want to capture some magic!
If you are not a Kahoot user yet, please consider: a) the Kahoots (quizzes) can be an excellent conversation starter (vs. assessment tool) b) the Kahoots can be modified to your liking (you can change the content)
here some screen-sharing capture to get a taste of the excitement:
Summary This short paper lays out an attempt to measure how much activity from Russian state-operated accounts released in the dataset made available by Twitter in October 2018 was targeted at the United Kingdom. Finding UK-related Tweets is not an easy task. By applying a combination of geographic inference, keyword analysis and classification by algorithm, we identified UK-related Tweets sent by these accounts and subjected them to further qualitative and quantitative analytic techniques.
We find:
There were three phases in Russian influence operations : under-the-radar account building, minor Brexit vote visibility, and larger-scale visibility during the London terror attacks.
Russian influence operations linked to the UK were most visible when discussing Islam . Tweets discussing Islam over the period of terror attacks between March and June 2017 were retweeted 25 times more often than their other messages.
The most widely-followed and visible troll account, @TEN_GOP, shared 109 Tweets related to the UK. Of these, 60 percent were related to Islam .
The topology of tweet activity underlines the vulnerability of social media users to disinformation in the wake of a tragedy or outrage.
Focus on the UK was a minor part of wider influence operations in this data . Of the nine million Tweets released by Twitter, 3.1 million were in English (34 percent). Of these 3.1 million, we estimate 83 thousand were in some way linked to the UK (2.7%). Those Tweets were shared 222 thousand times. It is plausible we are therefore seeing how the UK was caught up in Russian operations against the US .
Influence operations captured in this data show attempts to falsely amplify other news sources and to take part in conversations around Islam , and rarely show attempts to spread ‘fake news’ or influence at an electoral level.
On 17 October 2018, Twitter released data about 9 million tweets from 3,841 blocked accounts affiliated with the Internet Research Agency (IRA) – a Russian organisation founded in 2013 and based in St Petersburg, accused of using social media platforms to push pro-Kremlin propaganda and influence nation states beyond their borders, as well as being tasked with spreading pro-Kremlin messaging in Russia. It is one of the first major datasets linked to state-operated accounts engaging in influence operations released by a social media platform.
Conclusion
This report outlines the ways in which accounts linked to the Russian Internet ResearchAgency (IRA) carried out influence operations on social media and the ways their operationsintersected with the UK.The UK plays a reasonably small part in the wider context of this data. We see two possibleexplanations: either influence operations were primarily targeted at the US and British Twitterusers were impacted as collate, or this dataset is limited to US-focused operations whereevents in the UK were highlighted in an attempt to impact US public, rather than a concertedeffort against the UK. It is plausible that such efforts al so existed but are not reflected inthis dataset.Nevertheless, the data offers a highly useful window into how Russian influence operationsare carried out, as well as highlighting the moments when we might be most vulnerable tothem.Between 2011 and 2016, these state-operated accounts were camouflaged. Through manualand automated methods, they were able to quietly build up the trappings of an active andwell-followed Twitter account before eventually pivoting into attempts to influence the widerTwitter ecosystem. Their methods included engaging in unrelated and innocuous topics ofconversation, often through automated methods, and through sharing and engaging withother, more mainstream sources of news.Although this data shows levels of electoral and party-political influence operations to berelatively low, the day of the Brexit referendum results showed how messaging originatingfrom Russian state-controlled accounts might come to be visible – on June 24th 2016, we believe UK Twitter users discussing the Brexit Vote would have encountered messages originating from these accounts.As early as 2014, however, influence operations began taking part in conversations aroundIslam, and these accounts came to the fore during the three months of terror attacks thattook place between March and June 2017. In the immediate wake of these attacks, messagesrelated to Islam and circulated by Russian state-operated Twitter accounts were widelyshared, and would likely have been visible in the UK.The dataset released by Twitter begins to answer some questions about attempts by a foreignstate to interfere in British affairs online. It is notable that overt political or electoralinterference is poorly represented in this dataset: rather, we see attempts at stirring societaldivision, particularly around Islam in the UK, as the messages that resonated the most overthe period.What is perhaps most interesting about this moment is its portrayal of when we as socialmedia users are most vulnerable to the kinds of messages circulated by those looking toinfluence us. In the immediate aftermath of terror attacks, the data suggests, social mediausers were more receptive to this kind of messaging than at any other time.
It is clear that hostile states have identified the growth of online news and social media as aweak spot, and that significant effort has gone into attempting to exploit new media toinfluence its users. Understanding the ways in which these platforms have been used tospread division is an important first step to fighting it.Nevertheless, it is clear that this dataset provides just one window into the ways in whichforeign states have attempted to use online platforms as part of wider information warfare
and influence campaigns. We hope that other platforms will follow Twitter’s lead and release
similar datasets and encourage their users to proactively tackle those who would abuse theirplatforms.
Indiana University explores that question by bringing together tech partners and university leaders to share ideas on how to design classrooms that make better use of faculty and student time.
Untether instructors from the room’s podium, allowing them control from anywhere in the room;
Streamline the start of class, including biometric login to the room’s technology, behind-the-scenes routing of course content to room displays, control of lights and automatic attendance taking;
Offer whiteboards that can be captured, routed to different displays in the room and saved for future viewing and editing;
Provide small-group collaboration displays and the ability to easily route content to and from these displays; and
Deliver these features through a simple, user-friendly and reliable room/technology interface.
Key players from Crestron, Google, Sony, Steelcase and Spectrum met with Indiana University faculty, technologists and architects to generate new ideas related to current and emerging technologies. Activities included collaborative brainstorming focusing on these questions:
What else can we do to create the classroom of the future?
What current technology exists to solve these problems?
What could be developed that doesn’t yet exist?
What’s next?
top five findings:
Screenless and biometric technology will play an important role in the evolution of classrooms in higher education. We plan to research how voice activation and other Internet of Things technologies can streamline the process for faculty and students.
The entire classroom will become a space for student activity and brainstorming; walls, windows, desks and all activities are easily captured to the cloud, allowing conversations to continue outside of class or at the next class meeting.
Technology will be leveraged to include advance automation for a variety of tasks, so the faculty member is released from duties to focus on teaching.
The technology will become invisible to the process and enhance and customize the experience for the learner.
Virtual assistants could play an important role in providing students with a supported experience throughout their entire campus career.
In September 2015, the back-then library dean (they change every 2-3 years) requested a committee of librarians to meet and discuss the remodeling of Miller Center 2018. By that time the SCSU CIO was asserting the BYOx as a new policy for SCSU. BYOx in essence means the necessity for stronger (wider) WiFI pipe. Based on that assertion, I, Plamen Miltenoff, was insisting to shift the cost of hardware (computers, laptops) to infrastructure (more WiFi nods in the room and around it) and prepare for the upcoming IoT by learning to remodel our syllabi for mobile devices and use those (students) mobile devices, rather squander University money on hardware. At least one faculty member from the committee honestly admitted she has no idea about IoT and respectively the merit of my proposal. Thus, my proposal was completely disregarded by the self-nominated chair of the committee of librarians, who pushed for her idea to replace the desktops with a cart of laptops (a very 2010 idea, which by 2015 was already passe). As per Kelly (2018) (second article above), it is obvious the failure of her proposal to the dean to choose laptops over mobile devices, considering that faculty DO see mobile devices completely replacing desktops and laptops; that faculty DO not see document cameras and overhead projectors as a tool to stay.
Here are the notes from September 2015 https://blog.stcloudstate.edu/ims/2015/09/25/mc218-remodel/
As are result, my IoT proposal as now reflected in the Johnston (2018) (first article above), did not make it even formally to the dean, hence the necessity to make it available through the blog.
The SCSU library thinking regarding physical remodeling of classrooms is behind its times and that costs money for the university, if that room needs to be remodeled again to be with the contemporary times.
Lee, S.-H., Yeh, S.-C., Chan, R.-C., Chen, S., Yang, G., & Zheng, L.-R. (2016). Motor Ingredients Derived from a Wearable Sensor-Based Virtual Reality System for Frozen Shoulder Rehabilitation. BioMed Research International, 2016, 1–10. https://doi.org/10.1155/2016/7075464
Dvorkin, A. Y., Shahar, M., & Weiss, P. L. (2006). Reaching within Video-Capture Virtual Reality: Using Virtual Reality as a Motor Control Paradigm. CyberPsychology & Behavior, 9(2), 133–136. https://doi.org/10.1089/cpb.2006.9.133
Zeng, N., Pope, Z., Lee, J. E., & Gao, Z. (2018). Virtual Reality Exercise for Anxiety and Depression: A Preliminary Review of Current Research in an Emerging Field. Journal of Clinical Medicine, 7(3), 1-N.PAG. https://doi.org/10.3390/jcm7030042
Kramer, M., Honold, M., Hohl, K., Bockholt, U., Rettig, A., Elbel, M., & Dehner, C. (2009). Reliability of a new virtual reality test to measure cervicocephalic kinaesthesia. Journal of Electromyography & Kinesiology, 19(5), e353–e361. https://doi.org/10.1016/j.jelekin.2008.05.005
Cortes, N., Blount, E., Ringleb, S., & Onate, J. A. (2011). Soccer-specific video simulation for improving movement assessment. Sports Biomechanics, 10(1), 22–34. https://doi.org/10.1080/14763141.2010.547591
In his book, “Experience on Demand,” Jeremy Bailenson, the founding director of Stanford University’s Virtual Human Interaction Lab, writes, “No medium, of course can fully capture the subjective experience of another person, but by richly evoking a real-seeming, first-person experience, virtual reality does seem to promise to offer new, empathy-enhancing qualities.” Bailenson contrasts experiencing virtual reality with reading news accounts and watching documentaries. Those latter activities, he writes, require “a lot of imaginative work,” whereas virtual reality can “convey the feeling” of, say, a refugee camp’s environment, and the “smallness of the living quarters, the size of the camp.”
Caldwell—who used Google Expeditions to deliver a virtual reality experience set in the Holocaust—says that when his students first put on the goggles, they viewed them as a novelty. But within a minute or two, the students became quiet, absorbed in what they were seeing; they realized the “reality of the horror of what was in front of them.” Questions ensued.
Ron Berger, the Chief Academic Officer of EL Education, points to another factor schools should consider. He thinks virtual reality can be a powerful way to introduce kids to situations that require empathy or adopting different perspectives. However, he thinks no one tool or experience will bring results unless it is “nested in a broader framework of a vision and goals and relationships.”
Berger says virtual reality experiences have to be accompanied by work beforehand and follow-up afterwards. Kids, he says, need to be reflective and think critically.
immersion experiences like virtual reality should be “embedded in positive” adult and peer relationships. He adds that ideally, there’s also a resulting action where kids do something productive with the information they’ve learned, to help their own growth and to help others. He mentions an example where students interviewed local immigrants and refugees, then wrote the stories they heard. They published the stories in a book, and the profits went to legal fees for local refugees.
saving virtual reality for “very special experiences,” keeping it “relatively short” and not getting students dizzy or disoriented. A report Bailenson co-authored for Common Sense Media highlights the research that has—and has not—explored the effects of virtual reality on children. It states that the “potentially negative outcomes of VR include impacts on children’s sensory systems and vision, aggression, and unhealthy amounts of escapism and distraction from the physical world.”
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The Brain Science Is In: Students’ Emotional Needs Matter
What the neuro-, cognitive, and behavioral research says about social-emotional learning
Teachers, like parents, have always understood that children’s learning and growth do not occur in a vacuum, but instead at the messy intersection of academic, social, and emotional development.
• Malleability: Genes are not destiny. Our developing brains are largely shaped by our environments and relationships—a process that continues into adulthood.
• Context: Family, relationships, and lived experiences shape the physiological structure of our brains over time. Healthy amounts of challenge and adversity promote growth, but toxic stress takes a toll on the connections between the hemispheres of our brain.
• Continuum: While we’ve become familiar with the exponential development of the brain for young children, it continues throughout life. The explosion of brain growth into adolescence and early adulthood, in particular, requires putting serious work into much more intentional approaches to supporting that development than is common today.
Can We Please Stop Talking About Generations as if They Are a Thing?
Millennials are not all narcissists and boomers are not inherently selfish. The research on generations is flawed. DAVID COSTANZA
APRIL 13, 2018 9:00 AM
We spend a lot of time debating the characteristics of generations—are baby boomers really selfish and entitled, are millennials really narcissists, and the latest, has the next generation (whatever it is going to be called) already been ruined by cellphones? Many academics—and many consultants—argue that generations are distinct and that organizations, educators, and even parents need to accommodate them. These classifications are often met with resistance from those they supposedly represent, as most people dislike being represented by overgeneralizations, and these disputes only fuel the debate around this contentious topic.
In short, the science shows that generations are not a thing.
It is important to be clear what not a thing means. It does not mean that people today are the same as people 80 years ago or that anything else is static. Times change and so do people. However, the idea that distinct generations capture and represent these changes is unsupported.
What is a generation? Those who promote the concept define it as a group of people who are roughly the same age and who were influenced by a set of significant events. These experiences supposedly create commonalities, making those in the group more similar to each other and more different from other groups now and from groups of the same age in the past.
In line with the definition, there is a commonly held perception that people growing up around the same time and in the same place must have some sort of universally shared set of experiences and characteristics. It helps that the idea of generations intuitively makes sense. But the science does not support it. In fact, most of the research findings showing distinct generations are explained by other causes, have serious scientific flaws, or both.
Numerousbooks, articles, and pundits have claimed that millennials are much more narcissistic than young people in the past.
on average, millennials are no more narcissistic now than Xers or boomers were when they were in their 20s, and one study has even found they might be less so than generations past. While millennials today may be more narcissistic than Xers or boomers are today, that is because young people are pretty narcissistic regardless of when they are young. This too is an age effect.
Final example. Research shows that millennials joining the Army now show more pride in their service than boomers or Xers did when they joined 20-plus years ago. Is this a generational effect? Nope. Everyone in the military now shows more pride on average than 20 years ago because of 9/11. The terrorist attack increased military pride across the board. This is known as a period effect and it doesn’t have anything to do with generations.
Another problem—identifying true generational effects is methodologically very hard. The only way to do it would be to collect data from multiple longitudinal panels. Individuals in the first panel would be measured at the start of the study and then in subsequent years with new panels added every year thereafter, allowing assessment of whether people were changing because they were getting older (age effects), because of what was happening around them (period effects), or because of their generation (cohort effects). Unfortunately, such data sets pretty much do not exist. Thus, we’re never really able to determine why a change occurred.
According to one national-culture model, people from the United States are, on average, relatively individualistic, indulgent, and uncomfortable with hierarchical order. My note: RIchard Nisbett sides with Hofstede and Minkov: https://blog.stcloudstate.edu/ims/2016/06/14/cultural-differences/
Conversely, people from China are generally group-oriented, restrained, and comfortable with hierarchy. However, these countries are so large and diverse that they each have millions of individuals who are more similar to the “averages” of the other country than to their own.
Given these design and data issues, it is not surprising that researchers have tried a variety of different statistical techniques to massage (aka torture) the data in an attempt to find generational differences. Studies showing generational differences have used statistical techniques like analysis of variance (ANOVA) and cross-temporal meta-analysis (CTMA), neither of which is capable of actually attributing the differences to generations.
The statistical challenge derives from the problem we have already raised—generations (i.e., cohorts) are defined by age and period. As such, mathematically separating age, period, and cohort effects is very difficult because they are inherently confounded with one another. Their linear dependency creates what is known as an identification problem, and unless one has access to multiple longitudinal panels like I described above, it is impossible to statistically isolate the unique effect of any one factor.
Are some millennials narcissistic? Are some boomers selfish? Sure, but there are many who are not and whose profiles mirror othergenerations.
First, relying on flawed generational science leads to poor advice and bad decisions. An analogy: Women live longer than men, on average. Why? They engage in fewer risky behaviors, take better care of themselves, and have two X chromosomes, giving them backups in case of mutations. But if you are a man and you go to the doctor and ask how to live longer, she doesn’t tell you, “Be a woman.” She says eat better, exercise, and don’t do stupid stuff. Knowing the why guides the recommendation.
Now imagine you are a manager trying to retain your supposedly job-hopping, commitment-averse millennial employees and you know that Xers and boomers are less likely to leave their jobs. If you are that manager, you wouldn’t tell your millennial employees to “be a boomer” or “grow older” (nor would you decide to hire boomers or Xers rather than millennials—remember that individuals vary within populations). Instead, you should focus on addressing benefits, work conditions, and other factors that are reasons for leaving.
Second, this focus on generational distinctions wastes resources. Take the millennials-as-commitment-averse-job-hoppers stereotype. Based on this belief, consultants sell businesses on how to recruit and retain this mercurial generation. But are all (or even most) millennials job-hopping commitment avoiders? Survey research shows that millennials and Xers at the same point in their careers are equally likely to stay with their current employer for five or more years (22 percent v. 21.8 percent). It makes no sense for organizations to spend time and money changing HR policies when employees are just as likely to stick around today as they were 15 years ago.
Third, generations perpetuate stereotyping. Ask millennials if they are narcissistic job-hoppers and most of them will rightly be offended. Treat boomers like materialistic achievement seekers and see how it affects their work quality and commitment. We finally are starting to recognize that those within any specific group of people are varied individuals, and we should remember those same principles in this context too. We are (mostly) past it being acceptable to stereotype and discriminate against women, minorities, and the disabled. Why is it OK to do so to millennials or boomers?
The solutions are fairly straightforward, albeit challenging, to implement. To start, we need to focus on the why when talking about whether groups of people differ. The reasons why any generation should be different have only been generally discussed, and the theoretical mechanism that supposedly creates generations has not been fully fleshed out.
Next, we need to quit using these nonsensical generations labels, because they don’t mean anything. The start and end years are somewhat arbitrary anyway. The original conceptualization of social generations started with a biological generational interval of about 20 years, which historians, sociologists and demographers (for one example, see Strauss and Howe, 1991) then retrofitted with various significant historical events that defined the period.
The problem with this is twofold. First, such events do not occur in nice, neat 20-year intervals. Second, not everyone agrees on what the key events were for each generation, so the start and end dates also move around depending on what people think they were. One review found that start and end dates for boomers, Xers, and millennials varied by as many as nine years, and often four to five, depending on the study and the researcher. As with the statistical problem, how can distinct generations be a thing if simply defining when they start and when they end varies so much from study to study?
In the end, the core scientific problem is that the pop press, consultants, and even some academics who are committed to generations don’t focus on the whys. They have a vested interest in selling the whats(Generation Me has reportedly sold more than 115,000 copies, and Google “generations consultants” and see how many firms are dedicated to promulgating these distinctions), but without the science behind them, any prescriptions are worthless or even harmful
David Costanza is an associate professor of organizational sciences at George Washington University and a senior consortium fellow for the U.S. Army Research Institute. He researches, teaches, and consults in the areas of generations, leadership, culture, and organizational performance.
GOTTACATCHEMALL:EXPLORING POKEMON GO IN SEARCH OF LEARNING ENHANCEMENT OBJECTS
Annamaria Cacchione, Emma Procter-Legg and Sobah Abbas Petersen
Universidad Complutense de Madrid, Facultad de Filologia, Av.da Complutense sn, 28040 Madrid, Spain Independent; Abingdon, Oxon, UK SINTEF Technology and Society, Trondheim, Norway
The Augmented Reality Game, Pokemon Go, took the world by storm in the summer of 2016. City landscapes were decorated with amusing, colourful objects called Pokemon, and the holiday activities were enhanced by catching these wonderful creatures. In light of this, it is inevitable for mobile language learning researchers to reflect on the impact oft his game on learning and how it may be leveraged to enhance the design of mobile and ubiquitous technologies for mobile and situated language learning. This paper analyses the game Pokemon Go and the players’ experiences accordingto a framework developed for evaluating mobile language learning and discusses how Pokemon Go can help to meetsome of the challenges faced by earlier research activities.
A comparison between PG and Geocashing will illustrate the evolution of the concept of location-based games a concept that is very close to that of situated learning that we have explored in several previous works.
Pokémon Go is a free, location-based augmented reality game developed for mobile devices. Players useGPS on their mobile device to locate, capture, battle, and train virtual creatures (a.k.a. Pokémon), whichappear on screen overlaying the image seen through the device’s camera. This makes it seem like thePokemon are in the same real-world location as the player
“Put simply, augmented reality is a technology that overlays computer generated visuals over the real worldthrough a device camera bringing your surroundings to life and interacting with sensors such as location and heart rate to provide additional information”(Ramirez, 2014).
Apply the evaluation framework developed in 2015 for mobile learning applications(Cacchione, Procter-Legg, Petersen, & Winter, 2015). The framework is composed of a set offactors of different nature neuroscientific, technological, organisational and pedagogical and aim toprovide a comprehensive account of what plays a major role in ensuring effective learning via mobile devices
Apps like WhatsApp, Facebook, Snapchat, Instagram, Twitter, LinkedIn, Viber
Felix Krause described in 2017 that when a user grants an app access to their camera and microphone, the app could do the following:
Access both the front and the back camera.
Record you at any time the app is in the foreground.
Take pictures and videos without telling you.
Upload the pictures and videos without telling you.
Upload the pictures/videos it takes immediately.
Run real-time face recognition to detect facial features or expressions.
Livestream the camera on to the internet.
Detect if the user is on their phone alone, or watching together with a second person.
Upload random frames of the video stream to your web service and run a proper face recognition software which can find existing photos of you on the internet and create a 3D model based on your face.
For instance, here’s a Find my Phone application which a documentary maker installed on a phone, then let someone steal it. After the person stole it, the original owner spied on every moment of the thief’s life through the phone’s camera and microphone.
The government
Edward Snowden revealed an NSA program called Optic Nerves. The operation was a bulk surveillance program under which they captured webcam images every five minutes from Yahoo users’ video chats and then stored them for future use. It is estimated that between 3% and 11% of the images captured contained “undesirable nudity”.
Hackers can also gain access to your device with extraordinary ease via apps, PDF files, multimedia messages and even emojis.
An application called Metasploit on the ethical hacking platform Kali uses an Adobe Reader 9 (which over 60% of users still use) exploit to open a listener (rootkit) on the user’s computer. You alter the PDF with the program, send the user the malicious file, they open it, and hey presto – you have total control over their device remotely.
Once a user opens this PDF file, the hacker can then:
Install whatever software/app they like on the user’s device.
Use a keylogger to grab all of their passwords.
Steal all documents from the device.
Take pictures and stream videos from their camera.
Capture past or live audio from the microphone.
Upload incriminating images/documents to their PC, and notify the police.
And, if it’s not enough that your phone is tracking you – surveillance cameras in shops and streets are tracking you, too
You might even be on this website, InSeCam, which allows ordinary people online to watch surveillance cameras free of charge. It even allows you to search cameras by location, city, time zone, device manufacturer, and specify whether you want to see a kitchen, bar, restaurant or bedroom.