A revolution in classroom learning is underway. Students at Stevens Institute of Technology can now take an active role in contributing to class lessons, instead of passively listening to long lectures, because they have Mezzanine from Oblong Industries.
Customer
- Stevens Institute of Technology
Industry
- Education (for Finance)
Study Highlights
- Increased student participation
- Simultaneous content sharing
- Real-world scenarios and applications
Institutional Learning, Backed by Technology
Stevens Institute of Technology is shaping the next generation of leaders in the worlds of business and finance. Based in New Jersey, this leading high-tech finance education institution just across the river from Wall St. and Metropolitan New York. The school is recognized for its success in placing students into prestigious companies. To meet the demands of a more data-rich future, and to build upon its growing reputation as a leader in the field, Stevens invested in immersive visual collaboration technologies for its state-of-the art financial systems and data visualization labs.
Mezzanine in the Classroom
This investment turned the dynamics of the classroom on its head. The learning environment is now more collaborative with students able to immediately connect and contribute content streams from multiple sources simultaneously. This enables teams of student analysts to get significantly more engaged, evaluate problems from a wider angle, and move faster to better decisions. The educator in this environment can take on a supporting role as advisor, advocate, and guide to problem-solving the matters at hand. The approach is important, because it mirrors the realities of the future of work, which is forecast to be ever more complex.
Mezzanine for Finance
With the data deluge here now—the exponential increase in information coming ever faster and from every angle—tools are needed to help teams make sense of it. In the finance industry, markets and rates and opportunities are by definition influenced by myriad sources, expectations, timing, and trends. To win in the markets means you need great insights. Sure, A.I. and machine learning can provide filtration and prediction services but are you getting your answers from one source, or from multiple sources? Thorny problems require information and monitoring from multiple sources. You could consume source-data serially — first this chart and then that feed and then this other reference — but that takes a lot more brain power and retention than just putting everything up side-by-side where analysts can see it all together.
