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A University of Maryland-led team has received a $4.5 million grant from the Gates Foundation/Walton Family Foundation to improve artificial intelligence (AI) as a tool to strengthen math instruction and boost learning.

The researchers will develop a large-scale, open-source dataset for AI model training tools focused on K–12 math education, sourced over the next three years from classroom recordings of 300 instructors around the country who teach fourth to eighth graders.

Jing Liu, an assistant professor of education policy in UMD’s College of Education who is the lead principal investigator on the project, said the team aims to cover school districts from many different localities and that serve students from different socioeconomic backgrounds.

As the U.S. economy becomes more digitalized, employment in computer and information technology occupations is projected to grow by 10% in the next decade. In response to this increasing demand for technology skills, many states are working to expand computer science (CS) learning opportunities across K-12 classrooms. Thirty states have adopted a key part of Code.org Advocacy Coalition’s policy recommendations, which require all high schools to offer CS coursework, while eight states (and counting) have gone a step further in requiring all students to take CS as a high school graduation requirement. Despite heightened policy interest in CS access, however, research is surprisingly sparse on how the expansion of CS coursework affects postsecondary and labor market outcomes.

In a recent working paper, we aim to fill this research gap by investigating the impact of the expansion of high school CS course offerings. The State of Maryland, which is the context of our study, has rapidly expanded CS course offerings over the last decade. A 2018 law further requires all Maryland high schools to offer at least one “high-quality” CS course aligned with rigorous K-12 CS standards. These “high-quality” courses include foundational courses such as Computer Science Essentials, AP courses such as AP Computer Science Principles, and more specialized programming courses. They are also closely aligned with Code.org’s definition of “foundational” CS courses.

The sound of his teacher smacking his desk jolted Marquan into consciousness, and his head jerked up. “Wake up,” his teacher said.

Marquan hadn’t slept much the night before, and the words came out before he was fully coherent. “Watch out before you make me mad,” he said. 

His teacher turned and asked if that was a threat. The 16-year-old said no, he was just startled, but it was too late – he was sent out of the classroom and given a two-day suspension. 

In recent years high schools across the country have been adding computer science courses, and there is a movement to make them ubiquitous. A new study of an unusually rich dataset in Maryland found that such efforts can have a significant impact when it comes to getting more students to go on to careers in coding, and in bringing more diversity to the field.

The study, published as a working paper this month, found that taking a high-quality computer science course in high school increased the chance that the student goes on to major in computer science in college by 10 percentage points, and increased the chance that the student would finish a CS degree program by 5 percentage points.

“It’s not surprising in some ways,” says the lead researcher on the study, Jing Liu. “But we need the numbers so we can show it concretely.”

It’s been a year since ChatGPT stormed into classrooms. Its most common users have been students looking for homework aid—or shortcuts—and teachers who use it to create tailored, on-the-spot lesson plans.

A group of researchers, though, are asking a new question: Can generative AI help teachers teach better?

New research released in November shows that when teachers engage with frequent, personalized and on-demand feedback about their teaching practice, they ask richer, more analytical questions in their mathematics or science classes. The study, conducted by researchers from the University of Maryland, Harvard University, and Stanford University, also found that teachers engaged with feedback when it was directly emailed to them, suggesting that feedback needs to be provided in a succinct and accessible manner.

With technological breakthroughs in generative AI happening at unparalleled speed, it’s useful to remember the social nature of education and the consequent importance of teachers.

The free exchange of ideas through small-group work, the laughter that takes place naturally in the classroom, the curious eyes, and the raised hands—all reflect human learning. When the social elements are missing or compromised, as they were during COVID-19 school building closures, it is challenging for schools to help students engage and learn.

Students of color continue to be disciplined at higher rates than their white peers for the same behaviors—so much so that last month the Biden Administration warned schools that inequitable discipline practices could violate federal civil rights laws.

The top 5 percent of teachers most likely to refer students to the principal’s office for disciplinary action do so at such an outsized rate that they effectively double the racial gaps in such referrals, according to new research released today. These gaps are mainly driven by higher numbers of office discipline referrals (ODRs) issued for Black and Hispanic students, compared to White students.