Featured Post from University of Delaware

Exploring the Link Between Student Engagement and Deeper Learning

Apr 22, 2024

1 min

Amanda Jansen

When students are engaged in their learning, they are more likely to dive deeper into the material. This is because they are actively participating in the process, asking questions, and seeking out answers.


This is a concept that was recently explored by Amanda Jansen, a University of Delaware professor in the College of Education and Human Development. 


With attention to teachers’ first-person narratives and reflections, her study offers educators a model for understanding student engagement so that they can better facilitate deeper learning in their classrooms.



Funded by the National Science Foundation (NSF), it was found that the majority of the teachers in Jansen’s study understood the primary function of student engagement to be deeper learning about a mathematics concept rather than classroom performance. These teachers talked more about engagement in terms of cognitive, affective and social processes, and they often spoke about more than one dimension.


Jansen also emphasizes that how educators ask students to be engaged often reveals helpful information about educators’ instructional practices and their own assumptions. It can also send a message to students about their role in the learning process.


These more engaged students are more likely to analyze and evaluate information, rather than just passively accepting it. This higher level of thinking leads to a deeper understanding of the material and allows students to apply their knowledge in real-world situations.


To speak to Jansen about the study and its impact, click her "View Profile" button. 

Connect with:
Amanda Jansen

Amanda Jansen

Professor, School of Education

Prof. Jansen conducts research on mathematics teaching practices that support students' motivation and engagement.

Mathematics EducationTeacher EducationStudent MotivationStudent EngagementRough Draft Math

You might also like...

Check out some other posts from University of Delaware

2 min

Researchers laying the groundwork to eventually detect cerebral palsy via blood test

At the University of Delaware, molecular biologist Mona Batish in collaboration with Dr. Robert Akins at Nemours Children Hospital, is studying tiny loops in our cells called circular RNAs — once thought to be useless leftovers, but now believed to play an important role in diseases like cancer and cerebral palsy (CP). This is detailed in a new article in the Journal of Biological Chemistry. What are circular RNAs? They’re a special type of RNA that doesn’t make proteins but instead helps control how genes are turned on and off. Because they’re stable and can be found in blood, they may help doctors detect diseases more easily. So what’s the connection to cerebral palsy? CP is the most common physical disability in children, but right now it’s diagnosed only after symptoms appear — there’s no clear-cut test for it. Batish and her team are trying to change that. Working with researchers at Nemours Children’s Health, Batish discovered that in children with CP, a certain circular RNA — circNFIX — is found at much lower levels in muscle cells. This RNA normally helps the body make an important muscle-building protein called MEF2C. When circNFIX is missing or low, MEF2C isn’t made properly, which may lead to the weakened, shorter muscles seen in CP. This is the first time researchers have shown a link between circular RNAs and human muscle development in cerebral palsy. Why does this matter? If scientists can confirm this link, it could lead to: Earlier and more accurate diagnosis of CP using a simple blood test New treatments that help improve muscle development in affected children Batish’s ultimate goal? To create a test that can spot CP at birth — or even before — giving kids a better shot at early treatment and a higher quality of life. To speak to Batish, contact mediarelations@udel.edu. 

3 min

AI-powered model predicts post-concussion injury risk in college athletes

Athletes who suffer a concussion have a serious risk of reinjury after returning to play, but identifying which athletes are most vulnerable has always been a bit of a mystery, until now. Using artificial intelligence (AI), University of Delaware researchers have developed a novel machine learning model that predicts an athlete’s risk of lower-extremity musculoskeletal (MKS) injury after concussion with 95% accuracy. A recent study published in Sports Medicine details the development of the AI model, which builds on previously published research showing that the risk of post-concussion injury doubles, regardless of the sport. The most common post-concussive injuries include sprains, strains, or even broken bones or torn ACLs. “This is due to brain changes we see post-concussion,” said Thomas Buckley, professor of kinesiology and applied physiology at the College of Health Sciences. These brain changes affect athletes’ balance, cognition, and reaction times and can be difficult to detect in standard clinical testing. “Even a minuscule difference in balance, reaction time, or cognitive processing of what’s happening around you can make the difference between getting hurt and not,” Buckley said. How AI is changing injury risk assessment Recognizing the need for enhanced injury reduction risk tools, Buckley collaborated with colleagues in UD’s College of Engineering, Austin Brockmeier, assistant professor of electrical and computer engineering, and César Claros, a fourth-year doctoral student; Wei Qian, associate professor of statistics in the College of Agriculture and Natural Resources; and former KAAP postdoctoral fellow Melissa Anderson, who’s now an assistant professor at Ohio University. To assess injury risk, Brockmeier and Claros developed a comprehensive AI model that analyzes more than 100 variables, including sports and medical histories, concussion type, and pre- and post-concussion cognitive data. “Every athlete is unique, especially across various sports,” said Brockmeier. “Tracking an athlete’s performance over time, rather than relying on absolute values, helps identify disturbances, deviations, or deficits that, when compared to their baseline, may signal an increased risk of injury.” While some sports, such as football, carry higher injury risk, the model revealed that individual factors are just as important as the sport played. “We tested a version of the model that doesn’t have access to the athlete’s sport, and it still accurately predicted injury risk,” Brockmeier said. “This highlights how unique characteristics—not just the inherent risks of a sport—play a critical role in determining the likelihood of future injury,” said Brockmeier. The research, which tracked athletes over two years, also found that the risk of MSK injury post-concussion extends well into the athlete’s return to play. “Common sense would suggest that injuries would occur early in an athlete’s return to play, but that’s simply not true,” said Buckley. “Our research shows that the risk of future injury increases over time as athletes compensate and adapt to small deficits they may not even be aware of.” The next step for Buckey’s Concussion Research Lab is to further collaborate with UD Athletics’ strength and conditioning staff to design real-time interventions that could reduce injury risk. Beyond sports: AI’s potential in aging research The implications of the UD-developed machine-learning model extend far beyond sports. Brockmeier believes the algorithm could be used to predict fall risk in patients with Parkinson’s disease. Claros is also exploring how the injury risk reduction model can be applied to aging research with the Delaware Center for Cognitive Aging. “We want to use brain measurements to investigate whether baseline lifestyle measurements such as weight, BMI, and smoking history are predictive of future mild cognitive impairment or Alzheimer’s disease,” said Claros. To arrange an interview with Buckley, email UD's media relations team at MediaRelations@udel.edu

1 min

University of Delaware's physical therapy program ranked #1 graduate school in the U.S.

The University of Delaware's physical therapy program has been ranked #1 in the 2026 edition of Best Graduate Schools, U.S. News and World Report. The program has revolutionized the use of prosthetics, helped students become trainers for Super Bowl champions and boasts unique specialized training. Darcy Reisman, chair of the program, can talk about the following: Research: One study incorporated walking into daily behavior for stroke patients. Our PT researchers have also revolutionized the use of prosthetics to improve the lives of those who have lost limbs. Professional outcomes: Two of our PT grads were on the Philadelphia Eagles’ training staff during their Super Bowl run. Another is working in Major League Soccer with a Tennessee-based team. Specialized training: UDPT has an accredited Manual Fellowship Program that provides post-professional training for physical therapists in the specialized area of Manual Therapy.Research In total, U.S. news ranked 24 UD graduate programs among the best in the nation. Among those, 13 were in the top 50, including chemical engineering at #8.

View all posts
Powered by