Merging data science and education
This week's reading is based off of:
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Cheng, et al., 2013 “Reconsidering assessment in online/hybrid courses: Knowing versus learning”
van der Kleij et al. (2012) "Effects of feedback in a computer-based assessment for learning"
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Data science is a big field these days. Technology has enabled the ability to collect swathes of information on just about anything, from what you (will) shop for to how long you spend on a particular webpage. But it can also apply to education, which is what we learned with the guest speaker of the last lecture of the course.
As a graduate student training to become a computational materials scientist, this is pretty fascinating. The idea of finding patterns and trends, and then using those to predict behavior resonates with many people across disciplines. In materials, high throughput computations are being used to accelerate the process of finding new materials. Similar skills are used wherever data science can be applied.
For those interested in getting their feet wet with data science in education, I recommend checking out Kaggle, a web platform where data scientists can gather to solve data science related problems that are proposed from various companies, organizations, and non-profits. What is great about Kaggle is that anyone can choose to take on the challenge; you are only measured based on how well your code meets the task requirements. Particularly relevant is a Kaggle competition challenge about the College Scorecard (screenshot below), a measurement system from the U.S. Department of Education to elucidate the relationship between future income and university attendance. Check it out!
