For decades, universities, government agencies and foundations have been recruiting and retaining work to increase the diversity of engineering programs.

However, these efforts have not significantly increased the number of women, people of color, individuals with disabilities, and other underrepresented groups who study and earn degrees in STEM and engineering fields.

Latino, Black, Native American and Alaska native students have insufficient numbers of science and engineering degree recipients with bachelor’s degree and above. Among STEM workers with at least a bachelor’s degree, these groups are not in sufficient numbers.

Women are also inadequate in the STEM workforce and among beneficiaries in the fields of engineering, computer and information science.

I study fairness and social justice in STEM learning. In my recent research, I found that students from different backgrounds can do well in engineering courses if the course content is tailored to a wide range of learning preferences.

Why it matters

While serving as a program officer at the National Science Foundation, an independent federal agency supporting science and engineering, I reviewed a number of studies aimed at expanding the field of student enrollment and diversification.

Progress may work to promote diversity because university teachers do not consider synergies between content and learners.

The teacher is a mediator, and what is important is the student’s experience in the course.

It has long been widely believed that students have different learning styles. These include dynamics, learning through hands-on experience and physical exercise; listening, learning through listening information; and vision, learning through viewing information.

Recent research does not support the idea that teaching students in their learning style can improve learning.

That's why I prefer "learning preference" over learning styles. We all have preferences – whether it’s ice cream flavors, home decor or how we get information, including the way we learn.

Learning preferences are broader and more flexible, allowing for multiple ways to interact with content.

For example, suppose that the teacher always presents equations in the classroom and the student cannot understand. However, this is the only way to provide information. For individual learners, they fail. Some people will say, "These children cannot learn" and then ask students for help.

Then, spend years repeating the same cycle.

But if educators see it as clients, they can expand their perspective. If a customer is buying a shirt, they will look for shirts that attract their eyes. Eventually, they found someone they like.

Instructors need to take the same approach when trying to help students understand what is happening in the classroom. For example, if I have trouble with equations, I should give the option to participate in the course in a way that matches my learning preferences.

What's next?

A lot of research has been done in learning styles. However, content preferences have not been well explored.

In a truly democratic education system, curriculum design should reflect the voice of all stakeholders, not just those positions in power, i.e. lecturers.

Using data mining and artificial intelligence, educators have a variety of options to create content for a variety of preferences that learners may or need. For example, if students prefer other representative content, such as word problems, graphics, or simulations, AI can create various representations so that learners can get exposed to various representations.

I think future research needs to consider using technologies such as adaptive learning applications to understand students' learning preferences.

Prioritizing diverse learning perspectives in STEM can help create a more inclusive and responsive learning environment.

A brief introduction to the research is a brief view of interesting academic work.