July 11, 2022
A recently implemented university data visualization tool, designed by researchers from Cornell’s Future of Learning Lab, crunches years’ worth of undergraduate enrollment data and shows students the varied – and sometimes winding – academic paths Cornellians have forged from acceptance letter to diploma.
By Patricia Waldron
June 30, 2022
Most people have waited until the last minute to complete a school assignment at some point in their lives, but a new study finds that first-generation students and those belonging to underrepresented ethnic and racial groups turn in assignments later, on average, than their non-marginalized peers.
May 24, 2022
By Louis DiPietro
Cornell Tech will host the 2022 ACM Conference on Learning at Scale beginning Wednesday, June 1.
The three-day, hybrid conference gathers scientists and industry leaders in learning, computing, and data science who investigate digital learning environments that can support many learners, including online degree programs, mobile learning applications, intelligent tutoring systems, and more.
By Louis DiPietro
How do we learn to learn effectively? It’s a fundamental question at the heart of education and has particular resonance to leading online learning researchers like René Kizilcec, an assistant professor of Information Science in the Cornell Ann S. Bowers College of Computing and Informaiton Science.
By The Cornell Center for Social Sciences
The Cornell Center for Social Sciences grant program, which supports social science research by Cornell faculty members and conferences that directly benefit Cornell faculty and students, has awarded $142,636 for 15 proposals for fall 2021.
In classical Greek theater, the chorus served a specific purpose: to explain and comment on the particular moral issue being dramatized on stage.
These days, the “Greek chorus” – in the form of commenters on social media – can sometimes overshadow the “play” itself with an overabundance of zeal, mean-spiritedness and hubris.
To help struggling college students before it is too late, more and more universities are adopting machine-learning models to identify students at risk of dropping out.
What information goes into these models can have a big effect on how accurate and fair they are, especially when it comes to protected student characteristics like gender, race and family income. But in a new study, the largest audit of a college AI system to date, researchers find no evidence that removing protected student characteristics from a model improves the accuracy or fairness of predictions.
As the COVID-19 pandemic took hold in 2020, the list of things people could not do grew increasingly long.
But while going to the office, attending live events and gathering with large groups of friends became difficult or impossible, other activities grew in popularity – including online learning.
