Translate to multiple languages

Subscribe to my Email updates
Enjoy what you've read, make sure you subscribe to my Email Updates

Saturday, August 01, 2020

Researchers propose using AI to predict which college students might fail physics classes | Making sense of AI - VentureBeat

Kyle Wiggers, writes about artificial intelligence for VentureBeat, In a paper published on the preprint server, researchers affiliated with West Virginia University and California State Polytechnic University investigate the use of machine learning algorithms to identify at-risk students in introductory physics classes. 

Photo: via Shutterstock.
They claim it could be a powerful tool for educators and struggling college students alike, but critics argue technologies like it could harm those students with biased or misleading predictions. Physics and other core science courses form hurdles for science, technology, engineering, and mathematics (STEM) majors early in their college careers. (Studies show roughly 40% of students planning engineering and science majors end up switching to other subjects or failing to get a degree.) While physics pedagogies have developed a range of research-based practices to help students overcome challenges, some strategies have substantial per-class implementation costs. Moreover, not all are appropriate for every student.

It’s the researchers’ assertion that this calls for an algorithmic method of identifying at-risk students, particularly in physics. To this end, they build on previous work that used ACT scores, college GPA, and data collected within a physics class (such as homework grades and test scores) to predict whether a student would receive an A or B in the first and second semester.

But studies show AI is relatively poor at predicting complex outcomes even when trained on large corpora — and that it has a bias problem...  

“There is historic bias in higher education, in all of our society,” Iris Palmer, a senior advisor for higher education at think tank New America, told AMP Reports. “If we use that past data to predict how students are going to perform in the future, could we be baking some of that bias in? What will happen is they’ll get discouraged … and it’ll end up being a self-fulfilling prophecy for those particular students.” 

Source: VentureBeat