Education and Technology: Improving the Equity Odds

Charles (Chuck) Dziuban, Director Research Initiative for Teaching Effectiveness, University of Central Florida

A serious educational and economic equity problem exists in the United States. Findings from the Pell Institute show that students living in the bottom economic quartile have an eleven percent chance of completing a college degree.

Defined in opportunity costs, the odds against these youth are 9:1. Students living in the top economic quartile have a seventy-seven percent chance of completing college. Based on financial position alone, the odds in favor of their college success are 3:1. This is the following tail wind of wealth advantage inherited by the privileged class. The opportunity playing field is not close to even.

Additionally, the cost of post-secondary education is punishing. Recent estimates put accumulated college debt in the United Stated more than 1.7 trillion dollars, the majority of which encumbers the bottom economic quartile. If that debt comprised a gross domestic product (GDP), it would be the ninth-largest economy in the world.

Consider that corporations chose to spend over half a billion dollars on Super Bowl commercials this year. Such a number gives me pause about what we value. There are other troubling findings. For instance, although access to college for underserved students has increased, completion rates have not risen correspondingly-another inequitable outcome.

Consider the lives of students from that lower quartile. Often, they cannot take a full academic load because of their life demands. Like their more affluent peers, they can succeed, but accumulated stresses prevent them from effectively using their abilities.

These youth have no flexibility in their academic lives. Their situations are fragile and require a monumental balancing act, and should it fail, their optimal decision is to drop out with little chance of re-entering school. As James Baldwin says, “Anyone who has struggled with poverty knows how extremely expensive it is to be poor.” The bottom line is that we are wasting millions of young minds blocked by inequity that could be helping this country.

The question for the instructional technology business is: can any developments help solve the problem? Several are on the horizon: online and blended learning, artificial intelligence, quantum computing, big data, data science, agent-based modeling, and others. However, two developments, adaptive learning (AL) and predictive analytics (PA), have shown promise in recent years. At their core, both technologies seek to maximize students’ success by removing obstacles and improving the odds of success.

“Education is more than the sum of its individual components becoming more diverse, interdependent, connected, adaptive and increasingly sensitive to initial conditions”

Rapidly growing areas of artificial intelligence and big data and the advent of faster computer systems accommodate the fundamental principal of AL. If the time students have access to learning is the constant, then their knowledge acquisition will be the variable. On the other hand, if what they learn is the constant, then instructional time will be the variable. We have known this for years but have not had the technologies to make this a practicable application in education. This is no longer the case because excellent AL platforms can turn that old equation on its ear.

Of course, this approach requires educational institutions

(https://www.educationtechnologyinsights.com/cirrus-assessment) to reconsider their organizations. AL forces changes in traditional structures such as semesters and academic years. In an adaptive system, how will policies such as financial aid and grade reporting reframe themselves and what are the implications for instructors’ roles? These issues are challenging, but the potential for AL makes it worthwhile, particularly to mitigate the problems of students from underserved communities. An effective AL system can tell an instructor where students are with respect to the course content immediately, how they are progressing, where they are having difficulties and refer them to material that strengthens underlying knowledge, determines their preferred learning mode, monitors their interactions, and assesses revision and practice activities gauging their growth in the course. Although this may seem like big brother, it has succeeded in creating a new and more effective learning environments.

A second technology, PA, is demonstrating potential as well. At its core, PA seeks to identify as early as possible which students may be at-risk in a course or program so that instructional personnel can design support interventions as soon as possible.

Useful knowledge can come from data provided by student information and learning management systems. However, there is much more to PA than just statistical modeling. Prediction is the easy part; explaining in a meaningful way is much more difficult. Explanatory environmental analytics approaches have used concepts such as choice architecture and academic foci, giving students the opportunity to change majors while still maintaining momentum toward obtaining a degree. Another practice uses co-requisite courses taken simultaneously with the required version, showing higher success levels. In addition, colleges have identified gateway courses in small networks that lead to disproportionally higher success rates. Some institutions have provided students with progress and status dashboards, offering them a measure of control over where they stand and suggesting pathways to success in their courses. Innovative colleges have taken an entirely different approach by undertaking a careful forensic analysis of their policies and procedures, determining that they were the root cause of student failure and creating a new academic echo systems that support at-risk students.

Our educational system is becoming increasingly complex and chaotic, certainly exacerbated by the pandemic. However, that complexity will give rise to an emergent learning environment supported by new and effective instructional technologies. Education is more than the sum of its individual components becoming more diverse, interdependent, connected, adaptive and increasingly sensitive to initial conditions. Equity problems persist. However, the talent pool in our most underserved neighborhoods is as deep as in any gated community in the country. Interventions including AL and PA not been without challenges and missteps, but because we are gaining insight and understanding, technologies and students can coalesce into a fundamental component of the teaching-learning process. Solutions   are   on the horizon. We can do better. We have to.

Big Data

Financial

Artificial Intelligence

Predictive Analytics

Quantum Computing

Learning Management