Anna Egalite, assistant professor of leadership and policy at NC State. Previously, Anna taught elementary school and did a postdoc at Harvard. She’ll be writing about education-leadership research—what we know, where we have good intuitions, and where we’re still very much in the dark.
It’s back-to-school time and education reporters are highlighting stories about how school leaders are “leaning on data” to promote student learning, making administrative decisions that are “supported by a data-driven process,” and drawing on their experience in “data-driven instruction.”
Data Analytics a Key Skill for Administrators in K–12
A recent report highlights how data can open the door for K-12 school administrators to maximize student outcomes.
Eli Zimmerman
K-12 school districts looking to improve student success rates should invest in training administrators in data analysis, according to a report from the Data Quality Campaign.
Report authors also call on state policymakers to help lead the charge for more literate school administrators. School and district administrators need to model and support effective data use at every level, including as part of classroom instruction
A large global change in data protection law is about to hit the tech industry, thanks to the EU’s General Data Protection Regulations (GDPR). GDPR affects any company, wherever they are in the world, that handles data about European citizens. It becomes law on 25 May 2018, and as such includes UK citizens, since it precedes Brexit. It’s no surprise the EU has chosen to tighten the data protection belt: Europe has long opposed the tech industry’s expansionist tendencies, particularly through antitrust suits, and is perhaps the only regulatory body with the inclination and power to challenge Silicon Valley in the coming years.
So, no more harvesting data for unplanned analytics, future experimentation, or unspecified research. Teams must have specific uses for specific data.
Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.
According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”
Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.
People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.
The Stanford Women in Data Science conference (WiDS) is a one day global conference that will bring data scientists together to share cutting edge research. The conference aim is to inspire and encourage data scientists worldwide and exclusively support women in the field.
We will proudly host WiDS at The Alan Turing Institute. The conference will feature eminent female speakers through technical talks, lunchtime discussions on data science (topics to be announced shortly), a panel discussion and networking event.
The conference programme and speaker information will be soon available through the conference website. The event will be available worldwide via live streaming and the conference talks will be broadcast online.
The event will provide great opportunities to connect with potential mentors, collaborators and peers; hear about recent advancements in data science and explore new research dimensions.
Speakers:
Cecilia Lindgren
Mihaela van der Schaar
Jil Matheson
Codina Cotar
Emma McCoy
Cecilia Mascolo
Kathy Whaler
Mariana Damova
We welcome all regardless of gender to join us on Friday 6 April 2018 for an excellent learning experience.
Support analytics initiatives with data integration and governance. The changing landscape of enterprise IT is characterized by an expanding set of services, systems, and sourcing strategies. Data governance, cross-enterprise partnerships, and data integration are key ingredients in supporting higher education’s growing need for reliable information.
Enterprise IT Case Studies
In this set of EDUCAUSE Reviewcase studies, see how Drake University, the University of Tennessee, and the University of Montana improved their analytics initiatives through data integrations and governance.
A study that looked at reader engagement across articles that contained charts and infographics vs. articles that were text-only found that those with graphical storytelling, or what I like to call data storytelling, had up to 34 percent more comments and shares and a 300 percent improvement on the depth of scroll down the page.
Using storytelling techniques to present data not only makes it more visually appealing but also enables easy spotting of key trends, seamless results-tracking, and quick goal-monitoring.
Here are things that can help you build a bridge from your current methods to effective data storytelling–
Choose a topic by identifying your target audience, the goal of your visual, what you would like to achieve.
Organize your data by thinking about what you want to convey and then get rid of anything that doesn’t help you tell that story.
Spend time making your visualization look sharp by keeping it simple, using color and interactivity.
A few bonus tips to make your data visualizations really pop–
Don’t use more than two graphs at a time so as not to confuse participants.
Stick with one color per graph; making things multicolored will cause data to look jumbled.
Give context to your concept. Introduce your idea slowly and tell the story of what you want your data to reveal instead of assuming everyone in the room is on the same page.
Try using interactive data storytelling techniques to support your data.
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more on digital storytelling in this IMS blog
