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How will higher education change post-pandemic? Ipsos surveyed adults in 29 countries on how they see higher education being delivered in five years’ time.

How will higher education change post-pandemic?

The majority think the split between online and in-person learning that’s come about during the COVID-19 pandemic is here to stay. Respondents in China and Japan were most likely to see higher education being delivered mainly in person. Just over half of the adults surveyed believe in-person learning is worth its cost. In 2025, higher education will be a hybrid of in-person and online learning, according to a new Ipsos survey for the World Economic Forum. As a second wave of COVID-19 saw cases resurging across the globe in October, more than 27,500 adults in 29 countries were asked how they saw higher education being conducted in their country, five years from now. Pew Research Center. Complejidad de las Ciencias Sociales. Y de otras Ciencias y Disciplinas. Bioeconomía y biodesarrollo. Qué es eso de pedagogía y educación en complejidad.

La extraña idea del desarrollo. Genealogía de un concepto. Hipercomputacion biologica y comunicación entre los seres vivos. Carlos Eduardo Maldonado. El futuro ya está con nosotros. Acerca de la complejidad de la experiencia humana. Complejidad de la Bioética. IIW. Key Findings and Implications of the Science of Learning Development. 5 challenges for civil society in the Fourth Industrial Revolution. Watch any Hollywood science fiction movie, and there comes a point in every storyline where the advanced technologies meant to improve life start to show signs of impending disaster.

5 challenges for civil society in the Fourth Industrial Revolution

Typically this is when the skeptical character is finally heard, and the team of heroes regroups to make a plan to move forward together. Civil society often plays this kind of questioning role in our rapidly changing and fractured world. From faith-based charities and labour unions working to improve worker conditions in 18th- and 19th-century Great Britain to the rise of global NGOs and the development of public service innovation labs, civil society has constantly stood in the gap for workers, marginalized populations and others when the progress of industry and government during these industrial revolutions failed to trickle down.

Civil society organizations co-evolved in response to major breakthroughs and societal shifts of past industrial revolutions. Image: World Economic Forum 1. 2. 3. 4. 5. Share. The Four Purposes of Schooling – REENVISIONED. Organizing school for possibility instead of efficiency… There’s a lot of talk these days about shifting our schooling system (including by me here and here).

The Four Purposes of Schooling – REENVISIONED

But here’s the thing — the first step toward shifting a system is knowing what it’s meant to do. Systems change requires a clear understanding of both our purposes for schooling (why we have schooling), as well as our aims (our goals and vision)[i]. Yet current debates have largely lost their connection with both. We’re asking, “how can we reimagine school?” We mix together many different purposes and don’t make our assumptions explicit, which leads to a lot of confusion and poorly designed solutions.

As an example, recent education newsletters included the following headlines: 1) “Interventions can boost success of first generation and minority students in college”; 2) “Cultivating a growth mindset in mathematics”; 3) “Learning to assimilate: benefits of dual immersion programs”; In other words, Why School? Why School? Purposes vs. And, Databodies in Codespace. The Democratization of AI Is Putting Powerful Tools in the Hands of Non-Experts. The shortage of qualified data scientists is often highlighted as one of the major handbrakes on the adoption of big data and AI.

But a growing number of tools are putting these capabilities in the hands of non-experts, for better and for worse. There’s been an explosion in the breadth and quality of self-service analytics platforms in recent years, which let non-technical employees tap the huge amounts of data businesses are sitting on. They typically let users carry out simple, day-to-day analytic tasks—like creating reports or building data visualizations—rather than having to rely on the company’s data specialists.

Gartner recently predicted that workers using self-service analytics will output more analysis than professional data scientists. Given the perennial shortage of data specialists and the huge salaries they command these days, that’s probably music to the ears of most C-suite executives. They aren’t the only ones automating machine learning. TopLink.