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We all are familiar with the notion of the educational ladder. Most of us grew up with it. Beginning with preschool (for some), through kindergarten and elementary school to middle and high school. And then on to college (for some) with a sequence of certificates and degrees up through postdoctoral work.

Finishing one stage has always been the required qualification to begin the next. And each stage had a beginning, a middle and an end. It was up to the learner to adjust to the costs, the sequences, their rules and definitions of participation and success, and their determination of just what acceptable levels of knowledge were. While this necessarily brief and overgeneralized description applies to American higher education since the founding of our first colleges, it is especially pertinent in describing the evolution of the entire system since 1950.

To put it bluntly, the ways that we have offered higher education spring from inherent historical assumptions that dictated who could learn, where, what would be taught, what knowledge was appropriate for academic standing and how the knowledge would be shared and evaluated. In the last 50 years, we have pushed on those assumptions, welcoming more people in more modes of participation. But the basic assumptions have not changed. The traditional campus-based model is largely assumption driven.

This is the system and the sequence that is being disrupted as I write. And the driver for that disruption is a new, data-driven ecosystem of learning that can identify best practices, those which produce greater retention, more success, more focus on what needs to be learned and why, and higher levels of learning completion for all populations.

We are moving from assumption-driven practices to evaluated and data-driven practices. This disruptive change allows learners with vastly different backgrounds and needs to receive more respect for their talent and experience and also to assume greater participation in educational decisions that affect them. The new ecosystem and the data it generates gives us the potential and the tools to erase systemic discrimination from the educational blotter. Whether it has been intentional is not, in my opinion, the important point at this time.

It is important, however, to recognize that assumptions are discriminatory. They inherently favor some people and circumstances over others. Assumptions dictate winners and losers, while good data tell us which practices work better, producing more positive results, and which do not. The move from assumptions to data-driven best practices is the key to ending systemic discrimination in postsecondary education.

In the blog posts that follow, I will attempt to describe the steps in a basic learning cycle and how data-driven practices in the new ecosystem can drive better outcomes for all involved, thus significantly reducing and hopefully eliminating systemic discrimination in all its forms.

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