Data Must Speak: Positive Deviance Research

Curated on Posted on by Stefaan Verhulst

Report by UNICEF: “Despite the global learning crisis, even in the most difficult contexts, there are some “positive deviant” schools that outperform others in terms of learning, gender equality, and retention. Since 2019, in line with UNICEF’s Foundational Literacy and Numeracy Programme, Data Must Speak (DMS) research identifies these positive deviant schools, explores which behaviours and practices make them outperform others, and investigates how these could be implemented in lower performing schools in similar contexts. DMS research uses a sequential, participatory, mixed-methods approach to improve uptake, replicability, and sustainability. The research is being undertaken in 14 countries across Africa, Asia, and Latin America…(More)”.

The 5 Stages of Data Must Speak Research

Data Science for Social Impact in Higher Education:  First Steps

Curated on Posted on by Stefaan Verhulst

Data.org playbook: “… was designed to help you expand opportunities for social impact data science learning. As you browse, you will see a range of these opportunities including courses, modules for other courses, research and internship opportunities, and a variety of events and activities. The playbook also offers lessons learned to guide you through your process. Additionally, the Playbook includes profiles of students who have engaged in data science for social impact, guidance for engaging partners, and additional resources relating to evaluation and courses. We hope that this playbook will inspire and support your efforts to bring social impact data science to your institutions…

As you look at the range of ways you might bring data science for social impact to your students, remember that the intention is not for you to replicate what is here, but rather adapt them to your local contexts and conditions. You might draw pieces from several activities and combine them to create a customized strategy that works for you. Consider the assets you have around you and how you might be able to leverage them. At the same time, imagine how some of the lessons learned might reflect barriers you might face, as well. Most importantly, know that it is possible for you to create data science for social impact at your institution to bring benefit to your students and society…(More)”.

AI chatbots do work of civil servants in productivity trial

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Article by Paul Seddon: “Documents disclosed to the BBC have shed light on the use of AI-powered chatbot technology within government.

The chatbots have been used to analyse lengthy reports – a job that would normally be done by humans.

The Department for Education, which ran the trial, hopes it could boost productivity across Whitehall.

The PCS civil service union says it does not object to the use of AI – but clear guidelines are needed “so the benefits are shared by workers”.

The latest generation of chatbots, powered by artificial intelligence (AI), can quickly analyse reams of information, including images, to answer questions and summarise long articles.

They are expected to upend working practices across the economy in the coming years, and the government says they will have “significant implications” for the way officials work in future.

The education department ran the eight-week study over the summer under a contract with London-based company Faculty.ai, to test how so-called large language models (LLMs) could be used by officials.

The firm’s researchers used its access to a premium version of ChatGPT, the popular chatbot developed by OpenAI, to analyse draft local skills training plans that had been sent to the department to review.

These plans, drawn up by bodies representing local employers, are meant to influence the training offered by local further education colleges.

Results from the pilot are yet to be published, but documents and emails requested by the BBC under Freedom of Information laws offer an insight into the project’s aims.

According to an internal document setting out the reasons for the study, a chatbot would be used to summarise and compare the “main insights and themes” from the training plans.

The results, which were to be compared with summaries produced by civil servants, would test how Civil Service “productivity” might be improved.

It added that language models could analyse long, unstructured documents “where previously the only other option for be for individuals to read through all the reports”.

But the project’s aims went further, with hopes the chatbot could help provide “useful insights” that could help the department’s skills unit “identify future skills needs across the country”…(More)”.

How Leaders in Higher Education Can Embed Behavioral Science in Their Institutions

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Essay by Ross E. O’Hara: “…Once we view student success through a behavioral science lens and see the complex systems underlying student decision making, it becomes clear that behavioral scientists work best not as mechanics who repair broken systems, but as engineers who design better systems. Higher education, therefore, needs to diffuse those engineers throughout the organization.

To that end, Hallsworth recommends that organizations change their view of behavioral science “from projects to processes, from commissions to culture.” Only when behavioral science expertise is diffused across units and incorporated into all key organizational functions can a college become behaviorally enabled. So how might higher education go about this transformation?

1. Leverage the faculty

Leaders with deep expertise in behavioral science are likely already employed in social and behavioral sciences departments. Consider ways to focus their energy inward to tackle institutional challenges, perhaps using their own classrooms or departments as testing grounds. As they find promising solutions, build the infrastructure to disseminate and implement those ideas college and system wide. Unlike higher education’s normal approach—giving faculty additional unpaid and underappreciated committee work—provide funding and recognition that incentivizes faculty to make higher education policy an important piece of their academic portfolio.

2. Practice cross-functional training

I have spent the past several years providing colleges with behavioral science professional development, but too often this work is focused on a single functional unit, like academic advisors or faculty. Instead, create trainings that include representatives from across campus (e.g., enrollment; financial aid; registrar; student affairs). Not only will this diffuse behavioral science knowledge across the institution, but it will bring together the key players that impact student experience and make it easier for them to see the adaptive system that determines whether a student graduates or withdraws.

3. Let behavioral scientists be engineers

Whether you look for faculty or outside consultants, bring behavioral science experts into conversations early. From redesigning college-to-career pathways to building a new cafeteria, behavioral scientists can help gather and interpret student voices, foresee and circumvent behavioral challenges, and identify measurable and meaningful evaluation metrics. The impact of their expertise will be even greater when they work in an environment with a diffuse knowledge of behavioral science already in place…(More)”

Why we need applied humanities approaches

Curated on Posted on by Stefaan Verhulst

Article by Kathryn Strong Hansen: “Since the term “applied humanities” is not especially common, some explanation may be helpful. Applied humanities education prepares students to use humanities knowledge and methods in practice rather than only in theory. As the University of Arizona’s Department of Public and Applied Humanities puts it, the goal is “public enrichment and the direct and tangible improvement of the human condition.” While this goal undoubtedly involves “intrahumanities” outputs like museum and exhibit curation or textual editing, public enrichment through the humanities can also be pursued through science and engineering curricula.

The direct goal of much science education is improving the human condition, such as CRISPR developments opening up possibilities for gene therapies. Similarly, good engineering seeks to improve the human condition, like the LEED-certified building methods that minimize negative impacts on the environment.

Since the humanities concern themselves with the human experience in all its facets, they can offer much to STEM endeavors, and applied humanities approaches have been implemented for many decades. One of the most established applied humanities pursuits is applied linguistics, which has existed as a field of study since about 1948. Another useful and growing example is that of the medical humanities, which provide medical practitioners with training that can help them interact more effectively with patients and navigate the emotional impact of their profession.

While applied approaches might be less widespread or established in other humanities fields, they are just as needed. In part, they are needed because the skills and knowledge of humanities scholars can help students in a multiplicity of fields, including STEM disciplines, to improve their understanding of their subject matter and how it connects to society at large…(More)”.

Why Does Open Data Get Underused? A Focus on the Role of (Open) Data Literacy

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Paper by Gema Santos-Hermosa et al: “Open data has been conceptualised as a strategic form of public knowledge. Tightly connected with the developments in open government and open science, the main claim is that access to open data (OD) might be a catalyser of social innovation and citizen empowerment. Nevertheless, the so-called (open) data divide, as a problem connected to the situation of OD usage and engagement, is a concern.

In this chapter, we introduce the OD usage trends, focusing on the role played by (open) data literacy amongst either users or producers: citizens, professionals, and researchers. Indeed, we attempted to cover the problem of OD through a holistic approach including two areas of research and practice: open government data (OGD) and open research data (ORD). After uncovering several factors blocking OD consumption, we point out that more OD is being published (albeit with low usage), and we overview the research on data literacy. While the intentions of stakeholders are driven by many motivations, the abilities that put them in the condition to enhance OD might require further attention. In the end, we focus on several lifelong learning activities supporting open data literacy, uncovering the challenges ahead to unleash the power of OD in society…(More)”.

Algorithms of Education: How Datafication and Artificial Intelligence Shape Policy

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Book by Kalervo N. Gulson, Sam Sellar, and P. Taylor Webb: “While the science fiction tales of artificial intelligence eclipsing humanity are still very much fantasies, in Algorithms of Education the authors tell real stories of how algorithms and machines are transforming education governance, providing a fascinating discussion and critique of data and its role in education policy.

Algorithms of Education explores how, for policy makers, today’s ever-growing amount of data creates the illusion of greater control over the educational futures of students and the work of school leaders and teachers. In fact, the increased datafication of education, the authors argue, offers less and less control, as algorithms and artificial intelligence further abstract the educational experience and distance policy makers from teaching and learning. Focusing on the changing conditions for education policy and governance, Algorithms of Education proposes that schools and governments are increasingly turning to “synthetic governance”—a governance where what is human and machine becomes less clear—as a strategy for optimizing education.Exploring case studies of data infrastructures, facial recognition, and the growing use of data science in education, Algorithms of Education draws on a wide variety of fields—from critical theory and media studies to science and technology studies and education policy studies—mapping the political and methodological directions for engaging with datafication and artificial intelligence in education governance. According to the authors, we must go beyond the debates that separate humans and machines in order to develop new strategies for, and a new politics of, education…(More)”.

Code for What? Computer Science for Storytelling and Social Justice

Curated on Posted on by Stefaan Verhulst

Book by Clifford Lee and Elisabeth Soep: “Educators are urged to teach “code for all”—to make a specialized field accessible for students usually excluded from it. In this book, Clifford Lee and Elisabeth Soep instead ask the question, “Code for what?” What if coding were a justice-driven medium for storytelling rather than a narrow technical skill? What if “democratizing” computer science went beyond the usual one-off workshop and empowered youth to create digital products for social impact? Lee and Soep answer these questions with stories of a diverse group of young people in Oakland, California, who combine journalism, data, design, and code to create media that makes a difference.

These teenage and young adult producers created interactive projects that explored gendered and racialized dress code policies in schools; designed tools for LBGTQ+ youth experiencing discrimination; investigated facial recognition software and what can be done about it; and developed a mobile app to promote mental health through self-awareness and outreach for support, and more, for distribution to audiences that could reach into the millions. Working with educators and media professionals at YR Media, an award-winning organization that helps young people from underserved communities build skills in media, journalism, and the arts, these teens found their own vibrant answers to “why code?” They code for insight, connection and community, accountability, creative expression, joy, and hope…(More)”.

What competencies do public sector officials need to enhance national digital transformations?

Curated on Posted on by Stefaan Verhulst

Report by the Broadband Commission for Sustainable Development: “The Broadband Commission Working Group on AI Capacity Building has leveraged a multi-stakeholder leadership model to assess the critical capacity needs for public sector digital transformation, including from a developing country perspective. From interviews with policymakers, global and regional expert consultations and evaluation of current international practices, the Working Group has developed three competency domains and nine recommendations. The output is a competency framework for civil servants, spelling out the Artificial Intelligence and Digital Transformation Competencies needed today…(More)”

Math for Future Scientists: Require Statistics, Not Calculus

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Essay by Robert C. Thornett: “The common requirement to pass calculus in order to major in a science is a killer of students’ dreams. And it unnecessarily limits the pool of future scientists.

Charles Darwin is a classic example of a genius naturalist who was not a natural at math. As a young man, he sailed around the world aboard the HMS Beagle and explored the giant tortoises and iguanas of the Galapagos, the rainforests of Brazil, and the coral reefs of the South Pacific. From these sorts of direct engagements with nature, he developed his theory of evolution, which revolutionized science. But Darwin wrote in his autobiography that after studying math as a young man, he found that “it was repugnant to me.” When statistics stumped Darwin during his experiments investigating the advantages of crossbreeding plants, he called his cousin, the statistician Francis Galton, to try to make sense of the numbers.

Similarly, Thomas Edison said that as a boy he had a “distaste for mathematics.” But this did not stop him from becoming one of the most famous scientific inventors of all time. “I can always hire a mathematician,” said Edison, “but they can’t hire me.” Edison was so interested in chemistry that at the age of 13, when he got a job as a newsboy and concessionaire on the Grand Trunk Railroad, he brought a chemistry set aboard so he could do experiments during layovers. Math and science are distinctly different fields, and a talent for one does not imply a talent for the other.

According to professor emeritus Andrew Hacker of Queens College of the City University of New York, less than five percent of Americans will ever use any higher math at all in their jobs, including not only calculus but algebra, geometry, and trigonometry. And less than one percent will ever use calculus on the job. Born in 1929 and holding a PhD from Princeton, Hacker taught college political science for decades and has also been a math professor. His book The Math Myth: And Other STEM Delusions argues that not only college students but high school students should not be required to take algebra, geometry, trigonometry, or calculus at all. Hacker points out that not passing ninth grade algebra is the foremost academic indicator that a student will drop out of high school.

Before the objections tumble forth, I should emphasize that both Hacker and I like math and neither of us wants to remove all math requirements; we want to improve them. And I believe high school students should be required to study algebra and geometry. But Hacker’s larger argument is that both high schools and colleges should switch to teaching more useful types of math that can help students navigate the real world. He says American schools teach basic arithmetic well up to around middle school, but they stop there when they should continue teaching what he calls “adult arithmetic” or “sophisticated arithmetic” rather than veer off into more abstract types of math…(More)”.