Dear Commons Community,
The Chronicle of Higher Education reported yesterday that Rensselaer Polytechnic Institute has adopted a new “data dexterity” requirement for its students, starting in fall 2019. The requirement, the first of its kind in the nation, will propel all Rensselaer students beyond the current collegiate standard of “data literacy” to “data dexterity” — proficiency in using diverse datasets to define and solve complex real-world problems. The data requirement is part of an updated core curriculum — the common academic and non-academic elements that all Rensselaer students must complete to graduate – that reflects the skills and capabilities graduates need to be tomorrow’s global leaders and problem solvers. As reported by The Chronicle:
“The new requirement, which has a lot in common with more-traditional curricular areas like mathematics and data or quantitative literacy, will be fulfilled through two courses, one at the introductory level and another in a student’s major.
Kristin Bennett, a math professor there [R.P.I.], gave an example from her entry-level course, “Introduction to Data Mathematics.” Her students work in teams on a project to design drugs, and they devise computer models to predict how different chemicals interact. To do that, they might learn linear algebra or multivariate modeling, subjects that they wouldn’t get to until much later in a traditional curriculum. The new requirement at RPI, she said in an interview, can expose them to sophisticated material earlier, and in a tangible way — by allowing them to work on problems in complex projects with real-world applications.
“These problems are completely open ended,” Bennett said. “They kind of experience what it’s like in the real world to do math.”
Teaching students linear algebra earlier than they would traditionally encounter it also means that there are trade-offs, and Bennett has had to make tough choices about what material to leave out. Some concepts that students typically learn in lower-level courses will get short shrift; and she will teach linear algebra in what she calls “a narrow and deep way.”
Narrowing a curriculum so that students focus on — and hopefully learn — a smaller number of key concepts rather than covering a wide swath of content is a trade-off that I’ve heard many faculty members in lots of disciplines grapple with. Some professors have estimated that they have had to cut as much as a third of a course’s content when they adopted active-learning techniques instead of teaching via lecture.
This dynamic, in turn, mirrors an even larger situation facing many disciplines, says Lee Ligon, chair of the core-curriculum committee at RPI. Professors must make increasingly difficult choices about what material to include in their courses as data, research, and knowledge proliferate at an accelerating clip. They have to determine what new information students need to know while also weighing which pieces of foundational knowledge they should continue to teach. “We have to turn that around into a strength,” said Legon, an associate professor of biology, “to turn that overabundance of information around and use it as a lever to pry open new questions.”
At first, I thought this was just a more elaborate quantitative literacy requirement but it actually it requires a lot more problem solving. Interesting idea!