August 6, 2014 | 3
Scientific American’s August supplement takes a look at the changing landscape of education in the face of emerging technology, and asks the question, how do we increase interest and engagement in STEM initiatives? Learning in the Digital Age tackles issues of using big data to better understand students, the validity of online courses, and the need for new pedagogical methods. These themes were echoed in yesterday’s Second Annual Executive STEM Summit hosted by Scientific American and Macmillan Education where the message was overwhelmingly clear: if we want to see greater engagement in STEM, we must be willing to reimagine learning, along with the priorities and legacies we assign to it.
Data presented by Dr. Mark McDaniel, professor of psychology at Washington University in St. Louis, showed us that memorization and repetition don’t support long-term retention, but these are the methods we’re most likely to pursue as individuals when it comes time to try to learn. For example, when we first learn addition, we’ll practice that particular skill with a series of exercises of increasing complexity. This is also true for subtraction, multiplication and division. But it’s also true of art history: we learn to recognize the styles of great masters by looking at their work as a collection. We approach learning in blocks and it’s organized and straightforward. But this organized state might actually be hurting us. Learning to add against other mathematical equations may help us craft a story or experience that we can draw on later. A mixed approach allows us to be better at contextualizing real world examples that aren’t as clearly defined as our textbooks. Falling back to ordered approaches is a reasonable response to problem-solving—and that’s essentially what we’re asking students to do. One of McDaniel’s more salient’s points is that educators spend a great deal of time prepping students to learn, but then send them out to engage in learning on their own. If learning were a sport, students would have coaches, much as they do if they play baseball or basketball, but when it comes to learning there are no coaches. This isn’t due to a question of resources or efficiency although it’s easy to make that argument; instead, this seems seated in the social value placed in individual agency.
Education isn’t a team endeavor. While all students face the same curriculum, their performance is judged against each other; a diploma is an outcome that leads to a job. Education is separated from life—which perhaps is why working students sometimes struggle. This sense of the individual permeates almost everything touched by Western culture. Science, too, doesn’t always celebrate teams, though there seems to be a shift occurring in this area. It feels like a very singular, lonely pursuit. It’s the means to an outcome in the form of a job. But citizen science initiatives are showing us a different way: science and learning can happen outside of the laboratory and can connect people in novel ways. In these arenas, STEM emerges as a life skill, and excites passion and discussion in wider groups. Tapping into this conversation, will mean breaking pedestals—are we comfortable with that?
Backlash over the common core demonstrates how unsettling changes can be, but as Dr. Julia Phelan, Senior Researcher and Project Director at UCLA/CRESST said, “we’re living in the sandbox.” It would be ideal if we had a place to test methodology, but there is only the here and now, so we have to be willing to try. What does that mean? Beyond the usual complaints about laptops in the classroom, we have to look to use technology in new ways. For example, Arizona State University piloted a math program where the bulk of the work done by students happened in a computer lab. Their inputs were analyzed and lesson plans were tailored to each student strengths and weakness. The data from their work was also made available to a professor who tracked their process and offered support and guidance along the way at key points. At first glance, this model seems to perpetuate the idea of the unguided student, but the customization of the program actually provides the best possible conditions for that student to attain knowledge. Support and guidance are provided in relation to data about what that student needs, rather than a blanket curriculum that everyone must master at the same pace.
Working in this way could help bring learning to places where students have no opportunities for these pursuits—places like India where demand outstrips the supply of educators. Trials with massive open online courses (MOOCs) have not been entirely successful there perhaps because they fall flat in terms of engagement. Pawan Agarwal, an advisor for higher education in the Indian government, describes these efforts as having “limited technology and uneven quality,” but he’s optimistic for what technology can bring to the table. There may be room here for an adaptive model.
Critics are vocal that this sort of data-orientation will turn schools into factories, and nothing can replace a good teacher. I tend to agree with the latter point, but perhaps we should also be enabling our teachers to be better teachers. And perhaps there’s a way for technology to help.
Are you a teacher? What are your thoughts on technology in the classroom and STEM engagement?
For more on the lively discussions that followed during the course of the day, see #sciamlearning.
And, if you’re interested in bringing your ideas to the table, click here to learn about a grant made available from Macmillan Education for cross-sector STEM collaboration.
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