We have all seen the gradual and recent evolution to a more customer-centric focus in nearly all aspects of life, aided in part by technological developments, such as machine learning and artificial intelligence. Amazon in particular is a master of this, beginning with making it easy to purchase anything at a competitive price and having it delivered to your doorstep quickly, even within hours. More importantly, though, based on the analysis of prior purchases, purchasing preferences and patterns and other data analysis, Amazon recommends your next purchases. Other companies, with the goal of trying to anticipate customer needs and wants before they are even articulated, have deployed similar technology and strategies.
Other evidence of a shift to a customer-centric focus has been the open publishing of comparison ratings and customer reports, transparency in pricing, and acknowledgement of the ease of access to internet-based information, resulting in greater respect for a more informed consumer—even used car dealers offer CarFax reports so buyers can see a vehicle’s history. Clearly, the purchase of goods and services has dramatically changed and shifted from a provider focus to a consumer focus, with a heavy reliance on technology. Product ratings and store ratings have also changed to include robust customer reports.
In related developments, we have seen a similar shift in other sectors favoring the end-user rather than the provider. Healthcare providers have made things more convenient for patients with tools like online scheduling, online test results and online evaluations after every visit. This is supported by the proliferation of free-standing urgent care clinics and limited services clinics on-site within pharmacy chain stores. Food stores continue to sell the ingredients to make a great meal, but in response to consumer demand, they now offer many new chef-designed pre-packaged options. Automobile makers continue in an arms race to introduce driver-friendly options, from convenience features like heated and cooled seats to the extreme customer convenience like self-parking cars and self-driving vehicles.
In the higher education sector, some enterprising and attuned providers have adopted this “customer” orientation, scratching the surface of what is possible. Beginning with the admissions process, virtual tours, campus cameras and testimonials of current happy students via social media help to attract new students. Not only are courses offered online, but course selection, modality, location, pricing and even student support services have been tailored to support the student perspective and student desire. However, most colleges and universities remain locked in a published ranking arms race of sorts, where factors other than those that are of interest to students, especially non-traditional adult learners, are pursued.
But how might higher education change if it took a page from other sectors? As noted, many of these sectors have incorporated computer-based technologies and artificial intelligence to better understand and serve the end-user. Moreover, some have experimented with and exploited machine learning: the programming of computers to identify patterns in data to inform algorithms that can make data-driven predictions or decisions. Others have seized on the benefits of the “Internet of Things,” where common devices are connected to the Internet through a series of embedded wireless sensors that send data to cloud-based computer servers.
In his article 8 Ways Machine Learning Will Improve Education, author Tom Vander Ark highlights the following continued and expected contributions from machine learning that are quietly improving formal and informal education, including:
- Deep content analytics
- Learning analytics
- Dynamic scheduling
- Robust grading systems
- Process intelligence tools
- Predictive analytics
- Data mining
In addition, there are many administrative or back-office operations, such as bus scheduling, that are being improved and even matching of teachers and schools for best fit.
What if colleges and universities took advantage of some of these developments, followed the best practices from other industry sectors and applied machine learning to education? By way of example, what if each student had a personalized profile that would follow them throughout their life, not just for degree offerings, but also for non-credit professional development and enrichment, and not just at one institution? This profile would be robust and allow for student input. What if this profile could also precede learners with recommendations for courses, additional course readings or research, suggestions for internships or research opportunities or even incidental enrichment opportunities? Imagine the possibilities and power of creating a true lifelong learning portfolio for everyone. College rankings would take into account how successfully, or not, colleges and universities deployed such a system.
Beyond a lifelong learning portfolio, at the course and degree level things could get very interesting, much more informative, and I daresay, much more effective. Not only would grades be published online but each course would have a “CourseFax” report, not unlike CarFax, on how well students learned in a particular course, what learning outcomes were effective for a particular course, grade distribution and student comments about the course material and instructor. The CourseFax could also report the last time the course was updated, provide full disclosure about the readings, work products and graded assignments, indicate how students do in successive courses in this discipline and more. CourseFax would report on the instructor, other courses they teach now and have taught in the past and how students have performed in those courses. CourseFax would also provide evaluative data about instructors, such as how many articles or books they have published in what areas, how many students they have supervised, even how many students they have helped to land internships and jobs.
Learners could also fill out a profile, including career goals, which would be matched with courses that fit the profile. Blend in feedback from major employers and we can see at the course level how courses and their respective learning outcomes connect to employability, career paths and real-life problem solving. Such a robust system has the potential to move us from “happy sheets” and the banal reporting in websites, such as RateMyProfessor.com, where the issues discussed aren’t the effectiveness of learning in a course but rather whether an instructor is attractive and whether or not it is easy to get a high grade. Finally, imagine if all of these data were considered for the college ranking system rather than relying on GPA and standardized test scores.
While some will no doubt find this scandalous—especially at a time when college access and affordability remain top issues in higher education—learners would be well served and adult learners in particular would be able to make more informed and cost-effective education decisions that are more suited to their education and career goals.
We can do this. The technologies exists. Just ask Amazon or Google.
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